Erdas image analysis for arcgis

Geographic Imaging by Leica Geosystems GIS & Mapping

Using Image Analysis for ArcGIS

Julie Booth-Lamirand

Using the Image Analysis Extension for ArcGIS

Copyright © 2003 Leica Geosystems GIS & Mapping, LLCAll rights reserved.Printed in the United States of America.

The information contained in this document is the exclusive property of Leica Geosystems GIS & Mapping, LLC. This work is protected under United States copyright law and other international copyright treaties and conventions. No part of this work may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying and recording, or by any information storage or retrieval system, as expressly permitted in writing by Leica Geosystems GIS & Mapping, LLC. All requests should be sent to Attention: Manager of Technical Documentation, Leica Geosystems GIS & Mapping, LLC, 2801 Buford Highway NE, Suite 400, Atlanta, GA, 30329-2137, USA.

The information contained in this document is subject to change without notice.

CONTRIBUTORS

Contributors to this book and the On-line Help for Image Analysis for ArcGIS include: Christine Beaudoin, Jay Pongonis, Kris Curry, Lori Zastrow, Mladen Stojic′ , and Cheryl Brantley of Leica Geosystems GIS & Mapping, LLC.

U. S. GOVERNMENT RESTRICTED/LIMITED RIGHTS

Any software, documentation, and/or data delivered hereunder is subject to the terms of the License Agreement. In no event shall the U.S. Government acquire greater than RESTRICTED/LIMITED RIGHTS. At minimum, use, duplication, or disclosure by the U.S. Government is subject to restrictions set forth in FAR §52.227-14 Alternates I, II, and III (JUN 1987); FAR §52.227-19 (JUN 1987), and/or FAR §12.211/12.212 (Commercial Technical Data/Computer Software); and DFARS §252.227-7015 (NOV 1995) (Technical Data) and/or DFARS §227.7202 (Computer Software), as applicable. Contractor/Manufacturer is Leica Geosystems GIS & Mapping, LLC, 2801 Buford Highway NE, Suite 400, Atlanta, GA, 30329-2137, USA.

ERDAS, ERDAS IMAGINE, and IMAGINE OrthoBASE are registered trademarks. Image Analysis for ArcGIS is a trademark.

ERDAS® is a wholly owned subsidiary of Leica Geosystems GIS & Mapping, LLC.

Other companies and products mentioned herein are trademarks or registered trademarks of their respective trademark owners.

ContentsContents

Contents iii

tch 14

Foreword vii

Getting started

1 Introducing Image Analysis for ArcGIS 3Learning about Image Analysis for ArcGIS 10

2 Quick-start tutorial 11Exercise 1: Starting Image Analysis for ArcGIS 12Exercise 2: Adding images and applying Histogram StreExercise 3: Identifying similar areas in an image 18Exercise 4: Finding areas of change 22Exercise 5: Mosaicking images 30Exercise 6: Orthorectification of camera imagery 33What’s Next? 38

3 Applying data tools 39Using Seed Tool Properties 40Image Info 45Options 47

Working with features

4 Using Data Preparation 55Create New Image 56

III

Subset Image 58Mosaic Images 63Reproject Image 66

5 Performing Spatial Enhancement 69
Convolution 70Non-Directional Edge 75Focal Analysis 77Resolution Merge 79
6 Using Radiometric Enhancement 83LUT Stretch 84Histogram Equalization 87Histogram Matching 91Brightness Inversion 93

7 Applying Spectral Enhancement 95RGB to IHS 96IHS to RGB 99Vegetative Indices 101Color IR to Natural Color 104

8 Performing GIS Analysis 107Information versus data 108Neighborhood Analysis 109Thematic Change 111Recode 114Summarize Areas 120

9 Using Utilities 123Image Difference 124Layer Stack 126

USING IMAGE ANALYSIS FOR ARCGISIV

10 Understanding Classification 129The Classification Process 130

Classification tips 132
Unsupervised Classification/Categorize Image 134Supervised Classification 138Classification decision rules 140
11 Using Conversion 143Conversion 144Converting raster to features 145Converting features to raster 147

12 Applying Geocorrection Tools 149When to rectify 150Geocorrection property dialogs 153SPOT 158The Spot Properties dialog 160Polynomial transformation 161The Polynomial Properties dialog 168Rubber Sheeting 169Camera Properties 171IKONOS, QuickBird, and RPC Properties 173Landsat 177

Glossary 183

References 201

Index 205

CONTENTS V

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USING IMAGE ANALVI

VII

Fore

s capture a tains, and other ply recording ccur in the real shots of reality. record a specific ing cities, rivers,

need to be dy, easy-to-use in creating and y. Today’s features, es.

Where, What, done within the where is that is that? The new LC use imagery , so you can then

trial usage and aphy changes, so

word

An image of the earth’s surface is a wealth of information. Imagepermanent record of buildings, roads, rivers, trees, schools, mounfeatures located on the earth’s surface. But images go beyond simfeatures. Images also record relationships and processes as they oworld. Images are snapshots of geography, but they are also snapImages chronicle our earth and everything associated with it; theyplace at a specific point in time. They are snapshots of our changand mountains. Images are snapshots of life on earth.

The data in a GIS needs to reflect reality, and snapshots of realityincorporated and accurately transformed into instantaneously reainformation. From snapshots to digital reality, images are pivotalmaintaining the information infrastructure used by today’s societgeographic information systems have been carefully created withattributed behavior, analyzed relationships, and modeled process

There are five essential questions that any GIS needs to answer: When, Why, and How. Uncovering Why, When, and How are allGIS; images allow you to extract the Where and What. Preciselybuilding? What is that parcel of land used for? What type of tree extensions developed by Leica Geosystems GIS and Mapping, Lto allow you to accurately address the questions Where and Whatderive answers for the other three.

But our earth is changing! Urban growth, suburban sprawl, indusnatural phenomena continually alter our geography. As our geogr

YSIS FOR ARCGIS

does the information we need to understand it. Because an

USING IMAGE ANALVIII

image is a permanent record of features, behavior, relationships, and processes captured at a specific moment in time, using a series of images of the same area taken over time allows you to more accurately model and analyze the relationships and processes that are important to our earth.

The new extensions by Leica Geosystems are technological breakthroughs which allow you to transform a snapshot of geography into information that digitally represents reality in the context of a GIS. Image Analysis™ for ArcGIS and Stereo Analyst® for ArcGIS are tools built on top of a GIS to maintain that GIS with up-to-date information. The extensions provided by Leica Geosystems reliably transform imagery directly into your GIS for analyzing, mapping, visualizing, and understanding our world.

On behalf of the Image Analysis for ArcGIS and Stereo Analyst for ArcGIS product teams, I wish you all the best in working with these new products and hope you are successful in your GIS and mapping endeavors.

Sincerely,

Mladen Stojic′Product ManagerLeica Geosystems GIS & Mapping, LLC

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Image Analysis for ArcGIS

Image Analysis for ArcGIS™ is primarily designed for natural reinfrastructure management. The extension is very useful in the fieagriculture, environmental assessment, engineering, and infrastrucsuch as facility siting and corridor monitoring, and general geograupdate and maintenance.

Today, imagery of the earth’s surface is an integral part of desktoGIS, and it’s more important than ever to have the ability to provibackdrops to geographic databases and to be able to quickly updainvolving street use or land use data.

Image Analysis for ArcGIS gives you the ability to perform many

• Import and incorporate raster imagery into ArcGIS.• Categorize images into classes corresponding to land cover typ

vegetation.• Evaluate images captured at different times to identify areas of • Identify and automatically map a land cover type with a single • Find areas of dense and thriving vegetation in an image.• Enhance the appearance of an image by adjusting contrast and b

applying histogram stretches.• Align an image to a map coordinate system for precise area loc• Rectify satellite images through Geocorrection Models.

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izing land cover and erizing sites

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YSIS FOR ARCGIS

Updating databases

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USING IMAGE ANAL

re many kinds of imagery to choose from in a wide range of scales, spatial, and spectral resolutions, and map acraphy is often the choice for map updating because of its high precision. With Image Analysis for ArcGIS you are atify changes and make revisions and corrections to your geographic database.

Airphoto with shapefile of streets

5

Categorizing land cover and characterizing sites

nge of elevations, classes to help sensitive areas.

h. In this case the

INTROD

Transmand muidentify

The Clareas n

UCING IMAGE ANALYSIS FOR ARCGIS

ission towers for radio-based telecommunications must all be visible from each other, must be within a certain rast avoid fragile areas like wetlands. With Image Analysis for ArcGIS, you can categorize images into land cover suitable locations. You can use imagery and analysis techniques to identify wetlands and other environmentally

assification features enable you to divide an image into many different classes, and then highlight them as you wisot suitable for tower placement are highlighted, and the placement for the towers can be sited appropriately.

Classified image for radio towers

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Identifying and summarizing natural hazard damage

damage occurred. ge it sustained in

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file.

6

When vWith othe hur

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USING IMAGE ANAL

iewing a forest hit by a hurricane, you can use the mapping tools of Image Analysis for ArcGIS to show where thether ArcGIS tools, you can show the condition of the vegetation, how much stress it suffers, and how much damaricane.

Landsat images taken before and after the hurricane, in conjunction with a shapefile that identifies the forest bounison. Within the shapefile, you can see detailed tree stand inventory and management information.

The upper two pictures show the area in 1987 and in 1989 after Hurricane Hugo. The lower image features the shape

7

Identifying and monitoring urban growth and changes

aging that growth.

rban land use and resent how much

wth.

INTROD

Cities gYou ca

Here, Lland cothe city


row over time, and images give a good sense of how they grow, and how remaining land can be preserved by mann use Image Analysis for ArcGIS to reveal patterns of urban growth over time.

andsat data spanning 21 years was analyzed for urban growth. The final view shows the differences in extent of uver between 1973 and 1994. Those differences are represented as classes. The yellow urban areas from 1994 rep has grown beyond the red urban areas from 1973.

The top two images represent urban areas in red, first in 1974 and then in 1994. The bottom image shows the actual gro

8

Extracting features automatically

tic aperture radar

age gives you an

INTROD

Suppos(SAR)

The folexampl


e you are responsible for mapping the extent of an oil spill as part of a rapid response effort. You can use synthedata and Image Analysis for ArcGIS tools to identify and map the extent of such environmental hazards.

lowing image shows an oil spill of the northern coast of Spain. The first image shows the spill, and the second ime of how you can isolate the exact extent of a particular pattern using Image Analysis for ArcGIS.

Images depicting an oil spill off the coast of Spain and a polygon grown in the spill using Seed Tool.

9

Assessing vegetation stress

ls to identify and

tized and saved as quickly update

INTROD

Crops emonito

In thesea shapecrop m


xperience different stresses throughout the growing season. You can use multispectral imagery and analysis toor a crop’s health.

images, the Vegetative Indices function is used to see crop stress. The stressed areas are then automatically digifile. This kind of information can be used to help identify sources if variability in growth patterns. Then, you cananagement plans.

Crop stress shown through Vegetative Indices

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Learning about Image Analysis for ArcGISIS &

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USING IMAGE ANAL

n get a lot of information about the features of Image is for ArcGIS by accessing the online help. To browse the help contents for Image Analysis for ArcGIS, click Help e bottom of the Image Analysis menu. From this point you the Table of contents, index, or search feature to locate the ation you need. If you need online help for ArcGIS, click the ArcMap toolbar and choose ArcGIS Desktop Help.

Centers, Course Schedules, and Course Registrat

ESRI education solutions

ESRI provides educational opportunities related applications, and technology. You can choose amcourses, Web-based courses, and self-study workeducational solutions that fit your learning style For more information, visit the Web site www.es

re just learning about geographic information systems you may want to read the books about ArcCatalog and p: Using ArcCatalog and Using ArcMap. Knowing about pplications will make your use of Image Analysis for much easier.

re ready to learn about how Image Analysis for ArcGIS see the Quick-start tutorial. In the Quick-start tutorial, you’ll ow to adjust the appearance of an image, how to identify areas of an image, how to align an image to a feature theme, as finding areas of change and mosaicking images.

ing answers to questions

ok describes the typical workflow involved in creating and g GIS data for mapping projects. The chapters are set up so

u first learn the theory behind certain applications, then you oduced to the typical workflow you’d apply to get the results nt. A glossary is provided to help you understand any terms en’t seen before.

ng help on your computer

Contacting Leica Geosystems GMapping

If you need to contact Leica Geosystems for techthe product registration and support card you recAnalysis for ArcGIS. You can also contact Custo404/248-9777. Visit Leica Geosystems on the Wwww.gis.leica-geosystems.com.

Contacting ESRI

If you need to contact ESRI for technical supporttechnical support” in the Help system’s “Gettingsection. The telephone number for Technical Sup3744. You can also visit ESRI on the Web at ww

Leica Geosystems GIS & MappiEducation Solutions

Leica Geosystems GIS & Mapping Division offertraining about Image Analysis for ArcGIS. For mgot to the training Web site located at www.gis.lgeosystems.com. You can follow the training lin

11

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ck-start tutorial
Now that you know a little bit about the Image Analysis for ArcGits potential applications, the following exercises give you hands-using many of the extension’s tools. By working through the exegoing to use the most important components of the Image Analyextension and learn about the types of problems it can solve.

In Image Analysis for ArcGIS, you can quickly identify areas wicharacteristics. This is useful for identification in cases such as edisasters, burn areas or oil spills. Once an area has been defined,quickly saved into a shapefile. This avoids the need for manual dtutorial will show you how to use some Image Analysis for ArcGyou a good introduction to using Image Analysis for ArcGIS for needs.

HAPTER

Image Analysis for

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ctifying an image

YSIS FOR ARCGIS

Exercise 1: Start ing Image Analysis for ArcGIS

ArcGIS

tion to create a

enu, then click

2

1

USING IMAGE ANAL12

In the following exercises, we’ve assumed that you are using a single monitor or dual monitor workstation that is configured for use with ArcMap and Image Analysis for ArcGIS. That being the case, you will be lead through a series of tutorials in this chapter to help acquaint you with Image Analysis for ArcGIS and further show you some of the abilities of Image Analysis for ArcGIS.

In this exercise, you’ll learn how to start Image Analysis for ArcGIS and activate the toolbar associated with it. You will be able to gain access to all the important Image Analysis for ArcGIS features through its toolbar and menu list. After completing this exercise, you’ll be able to locate any Image Analysis for ArcGIS tool you need for preparation, enhancement, analysis, or geocorrection.This exercise assumes you have already successfully completed installation of Image Analysis for ArcGIS on your computer. If you have not installed Image Analysis for ArcGIS, refer to the installation guide packaged with the Image Analysis for ArcGIS CD, and install now.

Star ting Image Analysis for ArcGIS

1. Click the Start button on your desktop, then click Programs, and point to ArcGIS.

2. Click ArcMap to start the application.

Adding the Image Analysis for extension

1. If the ArcMap dialog opens, keep the opnew empty map, then click OK.

2. In the ArcMap window, click the Tools mExtensions.

1

13

3. In the Extensions dialog, click the check box for Image

to many of the sion. From the y different orrection type,

1

QUICK-START TUTORIAL

Analysis Extension to add the extension to ArcMap.

Once the Image Analysis Extension check box has been selected, the extension is activated.4. Click Close in the Extensions dialog.

Adding toolbars

1. Click the View menu, then point to Toolbars, and click Image Analysis to add that toolbar to the ArcMap window.

The Image Analysis toolbar is your gatewaytools and features you can use with the extenImage Analysis toolbar you can choose mananalysis types from the menu, choose a geocand set links in an image.

4

3

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Exercise 2: Adding images and apply ing Histogram Stretchew.

he view.

stretch applied u can apply

ta so that each f data points.

on can be found nt”.ontents, right o bring up

w, select RGB

pdown arrows

USING IMAGE ANAL14

Image data, displayed without any contrast manipulation, may appear either too light or too dark, making it difficult to begin your analysis. Image Analysis for ArcGIS allows you to display the same data in many different ways. For example, changing the distribution of pixels allows you to alter the brightness and contrast of the image. This is called histogram stretching. Histogram stretching enables you to manipulate the display of data to make your image easier to visually interpret and evaluate.

Add an Image Analysis for ArcGIS theme of Moscow

1. Open a new view. If you are starting this exercise immediately after Exercise 1, you should have a new, empty view ready.

2. Click the Add Data button .

3. In the Add Data dialog, select moscow_spot.tif, and click Add to draw it in the view. The path to the example data directory is ArcGIS\ArcTutor\ImageAnalysis.

4. Click Add to display the image in the vi

The image Moscow_spot.tif appears in t

Apply a Histogram Equalization

Standard deviations is the default histogram to images by Image Analysis for ArcGIS. Yohistogram equalization to redistribute the dadisplay value has roughly the same number oMore information about histogram equalizatiin chapter 6 “Using Radiometric Enhanceme1. Select moscow_spot.tif in the Table of c

click your mouse, and select Properties tLayer Properties.

2. Click the Symbology tab and under ShoComposite.

3. Check the Bands order and click the droto change any of the Bands.

3

4

15

You can also change the order of the bands in your current

ake sure x.

For this the future, you

t your output ge, and click age. from the Image tory you want to p will save you ing directory igate to it in

6


image by clicking on the color bar beside each band in the Table of contents. If you want bands to appear in a certain order for each image that you draw in the view, go to Tools\Options\Raster in ArcMap, and change the Default RGB Band Combinations.

4. Click the dropdown arrow and select Histogram Equalize as the Stretch Type.

5. Click Apply and OK.6. Click the Image Analysis menu dropdown arrow, point

to Radiometric Enhancement, and click Histogram Equalization.

7. In the Histogram Equalization dialog, mmoscow_spot.tif is in the Input Image bo

8. The Number of Bins will default to 256.exercise, leave the number at 256, but incan change it to suit your needs.

9. Navigate to the directory where you wanimages stored, type a name for your imaSave. The path will appear in Output Im

You can go to the Options dialog, accessibleAnalysis toolbar, and enter the working direcuse on the General tab of the dialog. This stetime by automatically bringing up your workwhenever you click the browse button to navorder to store an output image.

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2. If you want to see the histograms for the image, click the box.

1

2

3

4

USING IMAGE ANAL16

10. Click OK.The equalized image will appear in your Table of contents and in your view.

This is the histogram equalized image of Moscow.

Apply an Invert Stretch to the image of Moscow

In this example, you apply the Invert Stretch to the image to redisplay it with its brightness values reversed. Areas that originally appeared bright are now dark, and dark areas are bright. 1. Select the equalized file in the Table of contents, and

right-click your mouse. Click Properties and go to the Symbology tab.

Histograms button located in the Stretch3. Check the Invert box.

4. Click Apply and OK.

7

98

10

17
This is an inverted image of Moscow_spot.tif.You can apply different types of stretches to your image to emphasize different parts of the data. Depending on the original distribution of the data in the image, one stretch may make the image appear better than another. Image Analysis for ArcGIS allows you to rapidly make those comparisons. The Layer Properties Symbology tab can be a learning tool to see the effect of stretches on the input and output histograms. You’ll learn more about these stretches in chapter 6 “Using Radiometric Enhancement”.

YSIS FOR ARCGIS

Exercise 3: Ident i fy ing s imi lar areas in an image

l off the

called the s a polygon ilar and

ol, you will first start editing in rough these you want to a polygon. The rea the oil spill

ngle around the

USING IMAGE ANAL18

With Image Analysis for ArcGIS you can quickly identify areas with similar characteristics. This is useful for identification of environmental disasters or burn areas. Once an area has been defined, it can also be quickly saved into a shapefile. This action lets you avoid the need for manual digitizing. To define the area, you use the Seed Tool to point to an area of interest such as a dark area on an image depicting an oil spill. The Seed Tool returns a graphic polygon outlining areas with similar characteristics.

Add and draw an Image Analysis for ArcGIS theme depicting an oil spil l

1. If you are starting immediately after the previous exercise, clear your view by clicking the New Map File button on your ArcMap tool bar. You do not need to save the image. If you are beginning here, start ArcMap and load the Image Analysis for ArcGIS extension.

2. Click the Add Data button.3. In the Add Data dialog, select radar_oilspill.img, and

click Add to draw it in the view.

This is a radar image showing an oil spilnorthern coast of Spain.

Create a shapefi le

In this exercise, you use the Seed Tool (alsoRegion Growing Tool). The Seed Tool growgraphic in the image that encompasses all simcontiguous areas. In order to use the Seed Toneed to create a shapefile in ArcCatalog andorder to enable the Seed Tool. After going thsteps, you can point and click inside the areahighlight, in this case an oil spill, and create polygon enables you to see how much of an acovers.1. Click the Zoom In tool, and drag a recta

black area to see the spill more clearly.

1 2

19

g, click Import, dd from the up containing

ialog. drop it in the

the Table of

4

6


2. Click the ArcCatalog button. You can store the shapefile you’re going to create in the example data directory or navigate to a different directory if you wish.

3. Select the directory in the Table of contents and right click or click File, point to New, and click Shapefile.

4. In the Create New Shapefile dialog, name the new shapefile oilspill, and click the Feature Type dropdown arrow and select Polygon.

5. Check Show Details.6. Click Edit.

7. In the Spatial Reference Properties dialoand select radar_oilspill.img and click ABrowse for Dataset dialog that will pop the example data directory.

8. Click Apply and OK.9. Click OK in the Create New Shapefile d10. Select the oilspill shapefile, and drag and

ArcMap window. Oilspill will appear incontents.

11. Close ArcCatalog.

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3

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5

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cMap toolbar to

Map, and select

USING IMAGE ANAL20

Draw the polygon with the Seed Tool

1. Click the Image Analysis dropdown arrow, and click Seed Tool Properties.

2. Type a Seed Radius of 10 pixels in the Seed Radius text box.

3. Uncheck the Include Island Polygons box.The Seed Radius is the number of pixels surrounding the target pixel. The range of values of those surrounding pixels is considered when the Seed Tool grows the polygon.

4. Click OK.

5. Click the Editor toolbar button on the Ardisplay the Editor toolbar.

6. Click Editor on the Editor toolbar in ArcStart Editing.

8

7

2

3

4

1

21

y the Seed Tool.lygon in the h button at the

of the spill. An tent of this p of the oil.

5


7. Click the Seed Tool and click a point in the center of the oil spill. The Seed Tool will take a few moments to produce the polygon.

This is a polygon of an oil spill grown bIf you don’t automatically see the formed poimage displayed in the view, click the refresbottom of the view screen in ArcMap.You can see how the tool identifies the extentemergency team could be informed of the exdisaster in order to effectively plan a clean u

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Exercise 4: Finding areas of change

calculate the

w, click

USING IMAGE ANAL22

The Image Analysis for ArcGIS extension allows you to see changes over time. You can perform this type of analysis on either continuous data using Image Difference or thematic data using Thematic Change. In this exercise, you’ll learn how to use Image Difference and Thematic Change. Image Difference is useful for analyzing images of the same area to identify land cover features that may have changed over time. Image Difference performs a subtraction of one theme from another. This change is highlighted in green and red masks depicting increasing and decreasing values.

Find changed areas

In the following example, you are going to work with two continuous data images of the north metropolitan Atlanta, Georgia, area—one from 1987 and one from 1992. Continuous data images are those obtained from remote sensors like Landsat and SPOT. This kind of data measures reflectance characteristics of the earth’s surface, analogous to exposed film capturing an image. You will use Image Difference to identify areas that have been cleared of vegetation for the purpose of constructing a large regional shopping mall.

Add and draw the images of Atlanta

1. If you are starting immediately after the previous exercise, clear your view by clicking the New Map File button on your ArcMap tool bar. You do not need to save the image. If you are beginning here, start ArcMap and load the Image Analysis for ArcGIS extension.

2. Click the Add Data button.3. Press the Shift or Ctrl key, and click on

atl_spotp_87.img and atl_spotp_92.img in the Add Data dialog.

4. Click OK.

With images active in the view, you can difference between them.

Compute the difference due to development

1. Click the Image Analysis dropdown arroUtilities, and click Image Difference.

23

nges box.ore than box.ore than box.

t to store your the file, and

t to store your the file, and

.ence files iew.

3

8

2


2. In the Image Difference dialog, click the Before Theme dropdown arrow, and select Atl_spotp_87.img.

3. Click the After Theme dropdown arrow, and select Atl_spotp_92.img. 4. Choose As Percent in the Highlight Cha

5. Click the arrows to 15 in the Increases m6. Click the arrows to 15 in the Decreases m7. Navigate to the directory where you wan

Image Difference file, type the name of click Save.

8. Navigate to the directory where you wanHighlight Change file, type the name of click Save.

9. Click OK in the Image Difference dialogThe Highlight Change and Image Differappear in the Table of contents and the v

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6

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Image Difference calculates the difference in pixel values. e Difference sed than before green. Image 5 percent at has increased s now wet) and

the next portion e session by rcMap with enu, and click

matic Change tic data images. all possible nd cover class lar to Image n the same area ic Change can classified into data is a

a near Hagan ken in 1987 and ose you are the

ns a parcel of alysis for ur forested land

USING IMAGE ANAL24

Highlight Change shows the difference in red and green areas.

10. In the Table of contents, click the check box to turn off Highlight Change, and check Image Difference to display it in the view.

The Image Difference image shows the results of the subtraction of the Before Theme from the After Theme.

With the 15 percent parameter you set, Imagfinds areas that are at least 15 percent increa(designated clearing) and highlights them inDifference also finds areas that are at least 1decreased than before (designating an area thvegetation or an area that was once dry, but ihighlights them in red.

Close the view

You can now clear the view and either go to of this exercise, Thematic Change, or end thclosing ArcMap. If you want to shut down AImage Analysis for ArcGIS, click the File mExit. Click No when asked to save changes.

Using Thematic Change

Image Analysis for ArcGIS provides the Thefeature to make comparisons between themaThematic Change creates a theme that showscombinations of change and how an area’s lachanged over time. Thematic Change is simiDifference in that it computes changes betweeat different points in time. However, Thematonly be used with thematic data (data that is distinct categories). An example of thematicvegetation class map.This next example uses two images of an areLanding, South Carolina. The images were ta1989, before and after Hurricane Hugo. Suppforest manager for a paper company that owland in the hurricane’s path. With Image AnArcGIS, you can see exactly how much of yohas been destroyed by the storm.

25

Add the images of an area damaged by 1. Click the dropdown arrow in the Layers section of the _oct87.img is

w, point to ategorize.o make sure

ired Number of

t to store the

, and click

tion dialog.

3

5

1


Hurricane Hugo

1. If you are starting immediately after the previous exercise, clear your view by clicking the New Map File button on your ArcMap toolbar. You do not need to save the image. If you are beginning here, start ArcMap and load the Image Analysis for ArcGIS extension.

2. Open a new view and click Add Data.3. Press either the Shift key or Ctrl key, and select both

tm_oct87.img and tm_oct89.img in the Add Data dialog. Click Add.

This view shows an area damaged by Hurricane Hugo.

Create three classes of land cover

Before you calculate Thematic Change, you must first categorize the Before and After Themes. You can access Categorize through Unsupervised Classification, which is an option available from the Image Analysis dropdown menu. You’ll use the thematic themes created from those classifications to complete the Thematic Change calculation.

Image Analysis toolbar to make sure tmactive.

2. Click the Image Analysis dropdown arroClassification, and click Unsupervised/C

3. Click the Input Image dropdown arrow ttm_oct87.img is in the text box.

4. Click the arrows to 3 or type 3 in the DesClasses box.

5. Navigate to the directory where you wanoutput image, type the file name (use unsupervised_class_87 for this example)Save.

6. Click OK in the Unsupervised Classifica

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Using Unsupervised Classification to categorize continuous 4. Select Class 001, and double-click Class 001 under

l for Class 001,

002 under

l for Class 002,

003 under

l for Class 003,

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USING IMAGE ANAL26

images into thematic classes is particularly useful when you are unfamiliar with the data that makes up your image. You simply designate the number of classes you would like the data divided into, and Image Analysis for ArcGIS performs a calculation assigning pixels to classes depending on their values. By using Unsupervised Classification, you may be better able to quantify areas of different land cover in your image. You can then assign the classes names like water, forest, and bare soil.7. Click the check box of tm_oct87.img so the original

theme is not drawn in the view. This step makes the remaining themes draw faster in the view.

Give the classes names and assign colors to represent them

1. Double-click the title unsupervised_class_87.img to access the Layer Properties dialog.

2. Click the Symbology tab.3. Verify that Class_names is selected in the Value Field.

Class_names. Type the name Water.5. Double-click the color bar under Symbo

and choose blue from the color palette.6. Select Class 002, and double-click Class

Class_names. Type the name Forest.7. Double-click the color bar under Symbo

and choose green.8. Select Class 003, and double-click Class

Class_names. Type the name Bare Soil.

9. Double-click the color bar under Symboand choose a tan or light brown color.

10. Click Apply and OK.

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o select one of

read <From

working

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Categorize and name the areas in the post-hurricane image

1. Follow the steps provided for the theme tm_oct87.img on pages 25 and 26 under “Create three classes of land cover” and “Give the classes names and assign colors to represent them” to categorize the classes of the tm_oct89.img theme.

2. Click the box of the tm_oct89.img theme so that it does not draw in the view.

Recode to permanently write class names and colors to a f i le

After you have classified both of your images, you need to do a recode in order to permanently save the colors and class names you have assigned to the images. Recode lets you create a file with the specific images you’ve classified.1. Click the Image Analysis dropdown arrow, point to GIS

Analysis, and click Recode.

2. Click the Input Image dropdown arrow tthe classified images.

3. The Map Pixel Value through Field will view>. Leave this as is.

4. Click the browse button to bring up yourdirectory, and name the Output Image.

5. Click OK.

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Now do the same thing and perform a recode on the other 9. In the Symbology tab, double-click the symbol for was: t, is now Bare

nd click Apply. e any color you

red. The red oil.

the

caused by the how much any’s land.

USING IMAGE ANAL28

classified image you did of the Hugo area. Both of the images will have your class names and colors permanently saved.

Use Thematic Change to see how land cover changed because of Hugo

1. Make sure both recoded images are checked in the Table of contents so both will be active in the view.

2. Click the Image Analysis dropdown arrow, point to GIS Analysis, and click Thematic Change.

3. Click the Before Theme dropdown arrow and select the 87 classification image.

4. Click the After Theme dropdown arrow, and select the 89 classification image.

5. Navigate to the directory where you want to store the Output Image, type the file name, and click Save.

6. Click OK.7. Click the check box of Thematic Change to draw it in

the view.8. Double-click the Thematic Change title to access Layer

Properties.

Class 002, is now: Class 003 (was ForesSoil) to access the color palette.

10. Click the color red in the color palette, aYou don’t have to choose red, you can uslike.

11. Click OK.

You can see the amount of destruction inshows what was forest and is now bare s

Add a feature theme that showsproperty boundary

Using Thematic Change, the overall damagehurricane is clear. Next, you will want to seedamage actually occurred on the paper comp1. Click Add Data.2. Select property.shp, and click Add.

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astation within s.

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Thematic Change image with the property shapefile

Make the property transparent

1. Double-click on the property theme to access Layer Properties.

2. Click the Symbology tab, and double-click the color symbol.

3. In the Symbol Selector, click the Hollow symbol.4. Click the Outline Width arrows, or type the number 3 in

the box.5. Click the Outline Color dropdown arrow, and choose a

color that will easily stand out to show your property line.

6. Click OK.7. Click Apply and OK on the Symbology tab.

The yellow outline clearly shows the devthe paper company’s property boundarie

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Exercise 5: Mosaicking imageshe Mosaic tool hichever is on e.

ur mouse.

solution. You .

images in the

USING IMAGE ANAL30

Image Analysis for ArcGIS allows you to mosaic multiple images. When you mosaic images, you join them together to form one single image that covers the entire area. To mosaic images, simply display them in the view, ensure that they have the same number of bands, then select Mosaic.In the following exercise, you are going to mosaic two airphotos with the same resolution.

Add and draw the images

1. If you are starting immediately after the previous exercise, clear your view by clicking the New Map File button on your ArcMap tool bar. You do not need to save the image. If you are beginning here, start ArcMap and load the Image Analysis for ArcGIS extension with a new map.

2. Click the Add Data button.3. Press the Shift key and select Airphoto1.img and

Airphoto2.img in the Add Data dialog. Click Add.4. Click Airphoto1.img and drag it so that it is at the top of

the Table of contents.

The two airphotos display in the view. Tjoins them as they appear in the view: wtop is also on top in the mosaicked imag

Zoom in to see image details

1. Select Airphoto1.img, and right-click yo2. Click Zoom to raster resolution.

The two images are displayed at a 1:1 recan now use Pan to see how they overlap

3. Click the Pan button, then maneuver theview.

31

w, point to Data

own arrow and


This illustration shows where the two images overlap.4. Click the Full Extent button so that both images display

their entirety in the view.

Use Mosaic to join the images

1. If you want to use some other extent than Union of Inputs for your mosaic, you must first go to the Extent tab in the Options dialog and change the Extent before opening Mosaic Images. After opening the Mosaic Images dialog, you cannot access the Options dialog. However, it is recommended that you keep the default of Union of Inputs for mosaicking. 2. Click the Image Analysis dropdown arro

Preparation, and click Mosaic Images.3. Click the Handle Images overlaps dropd

choose Use Order Displayed.

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4. If you want to automatically crop your images, check

es as they 1 is mosaicked

USING IMAGE ANAL32

the box, and use the arrows or type the percentage by which to crop the images.

5. Choose Brightness/Contrast as the Color Balancing option.

6. If you have changed the extent to something other than Union of Inputs, check this box, but for this exercise you will need to leave the extent set at Union of Inputs and the box unchecked.

7. Navigate to the directory where you want to save your files, type the file name, and click Save.

8. Click OK.

The Mosaic function joins the two imagappear in the view. In this case Airphotoover Airphoto2.

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Exercise 6: Orthorect i f icat ion of camera imageryr the image

for the data

nts and right and right click. nu to bring up

stem, click

7


The Image Analysis for ArcGIS extension for ArcGIS has a feature called Geocorrection Properties. The function of this feature is to rectify imagery. One of the tools that makes up Geocorrection Properties is the Camera model.In this exercise you will orthorectify images using the Camera model in Geocorrection Properties.

Add raster and feature datasets

1. If you are starting immediately after the previous exercise, clear your view by clicking the New Map File button on your ArcMap tool bar. You do not need to save the image. If you are beginning here, start ArcMap and load the Image Analysis for ArcGIS extension with a new map.

2. Click the Add Data button.3. Hold the Shift key down and select both ps_napp.img

and ps_streets.shp in the Add Data dialog. Click Add.4. Right click on ps_napp.img and click Zoom to Layer.

The images are drawn in the view. You can see the fiducial markings around the edges and at the top.

Select the coordinate system fo

This procedure defines the coordinate systemframe in Image Analysis for ArcGIS.1. Either select Layers in the Table of conte

click, or move your cursor into the view2. Select Properties at the bottom of the me

the Data Frame Properties dialog.

3. Click the Coordinate System tab.4. In the box labeled Select a coordinate sy

Predefined.

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5. Click Projected Coordinate Systems, and then click 4. Navigate to the ArcGIS ArcTutor directory, and choose

w and select

, and select

or X and 0.000

er of Fiducials.re the number

4

USING IMAGE ANAL34

Utm.6. Click NAD 1927, then click NAD 1927 UTM Zone

11N.7. Click Apply, and click OK.

Orthorectifying your image using Geocorrection Properties

1. Click the Model Types dropdown arrow, and click Camera.

2. Click the Geocorrection Properties button on the toolbar to open the Camera dialog.

3. Click the Elevation tab, and select File to use as the Elevation Source.

ps_dem.img as the Elevation File.5. Click the Elevation Units dropdown arro

Meters.6. Check Account for Earth’s curvature.

7. Click the Camera tab.8. Click the Camera Name dropdown arrow

Default Wild.9. In the Principal Point box, enter -0.004 f

for Y.10. Enter a Focal Length of 152.804.11. Click the arrows, or type 4 for the numb12. Click in the Film X and Film Y box whe

of Fiducials will reduce to 4.

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35

13. Type the following coordinates in the corresponding 2. Click the Green fiducial, and the software will take you ducial sshair.

in until you can shair there. The r points where l marker.

ure to click click on the

to Layer. You file are now an square error the Camera than 1.0. Now,

1

2


fiducial spaces. Use the Tab key to move from space to space.

1. -106.000 106.0002. 105.999 105.9943. 105.998 -105.9994. -106.008 -105.999

14. Name the camera in the Camera Name box.15. Click Save to save the camera information with the

Camera Name.16. Click Apply and move to the next section.

Fiducial placement

1. Click the Fiducials tab, and make sure the first fiducial orientation is selected.

to the approximate location of the first fiplacement. Your cursor has become a cro

3. Click the Fixed Zoom In tool, and zoom see the actual fiducial, and click the crossoftware will take you to each of the fouyou can click the crosshair in the fiducia

When you are done placing fiducials, make sApply then OK to close. You can then right image in the Table of contents, and click Zoomwill notice that both the image and the shapedisplayed in the view. To look at the root me(RMSE) on the fiducials tab, you can reopenProperties dialog. The RMSE should be lessit is time to rectify the images.

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e this:

USING IMAGE ANAL36

After placing fiducials, both the image and the shapefile are shown in the view for rectification.

Plac ing l inks

1. Click the Add Links button.

2. Looking closely at the image and shapefile in the view, and using the next image as a guide, line up where you should place the first link. Follow the markers in the next image to place the first three links. You will need to click the crosshair on the point in the image first and then drag the cursor over to the point in the shapefile where you want to click.

Your first link should look approximately lik

3. Place links 2 and 3.

37

After placing the third link, your image should look Your image should warp and become aligned with the streets a rectangle

t more clearly.

s tab of Camera age Analysis


something like this:

4. Zoom to the upper left portion of the image, and place a link according to this next image.

5. Zoom to the lower left portion of the image, and place a link according to the previous image.

shapefile. You can use the Zoom tool to drawaround the aligned area and zoom in to see i

Now take a look at the RMS Error on the LinkProperties. You can go to Save As on the Immenu and save the image if you wish.

YSIS FOR ARCGIS

What’s Next?

USING IMAGE ANAL38

This tutorial has introduced you to some features and basic functions of Image Analysis for ArcGIS. The following chapters go into greater detail about the different tools and elements of Image Analysis for ArcGIS, and include instructions on how to use them to your advantage.

39

3App

there are three l three aid you in e results that are

gons of similar

ecalculate

.

IN THIS C

• Seed To

• Image In

• Options

3
lying data tools
You will notice when you look at the Image Analysis menu that choices called Seed Tool Properties, Image Info, and Options. Almanipulating, analyzing, and altering your data so you can produceasier to interpret than they would be with no data tool input.

• Seed Tool Properties automatically generates feature layer polyspectral value.

• Image Info gives you the ability to apply a NoData Value and rstatistics.

• Options lets you change extent, cell size, preferences, and more

HAPTER

ol Properties

fo

YSIS FOR ARCGIS

Using Seed Tool Propert ies

Tool is controlled of pixels of the e Analysis menu.

pixels. The Image els.

eed Tool is when s includes more to grow the . A smaller Seed

Setting the Seed ing over pixels

e image. This can ous area might lues like

40

As statTool Prof simiyou canand draYou catool onbest wi

In ordethe imaArcCatname itEditingOnce yback to

The bathe currone banin vegeof that green, statistic

When athe Seegraphicby the

Cont

You caAnalysshapefiyou wacan ope

USING IMAGE ANAL

s in each band of data before creating the polygon.

polygon shapefile is being edited, a polygon defined using d Tool is added to the shapefile. Like other ArcGIS s, you can change the appearance of the polygon produced

Seed Tool using the Graphics tools.

roll ing the Seed Tool

n use the Seed Tool simply by choosing it from the Image is toolbar and clicking on an image after generating a le. The defaults usually produce a good result. However, if nt more control over the parameters of the Seed Tool, you n Seed Tool Properties from the Image Analysis menu.

pixels to calculate the range of pixel values usedpolygon, and typically produces a larger polygonRadius uses fewer pixels to determine the range.Radius to 0.5 or less restricts the polygon to growwith the exact value as the pixel you click on in thbe useful for thematic images in which a contiguhave a single pixel value, instead of a range of vacontinuous data.

ed in the opening of the chapter, the main function of Seed operties is to automatically generate feature layer polygons lar spectral value. After creating a shapefile in ArcCatalog, either click in an image on a single point, or you can click g a rectangle in a portion of the image that interests you. n decide which method you wish to use before clicking the the toolbar, or you can experiment with which method looks th your data.

r to use the Seed Tool, you must first create the shapefile for ge you are using in ArcCatalog. You will need to open alog, create a new shapefile in the directory you want to use, , choose polygon as the type of shapefile, and then use Start on the Editor toolbar in ArcMap to activate the Seed Tool. ou are finished and you have grown the polygon, you can go the Editor toolbar and select Stop Editing.

nd or bands used in growing the polygon are controlled by ent visible bands as set in Layer Properties. If you only have d displayed, such as the red band, when you are interested

tation analysis, then the Seed Tool only looks at the statistics band to create the polygon. If you have all the bands (red, and blue) displayed, then the Seed Tool evaluates the

Seed Tool dialog

Seed Radius

When you use the simple click method, the Seed by the Seed Radius. You can change the numberSeed Radius by opening the dialog from the ImagFrom this dialog, you select your Seed Radius in Analysis for ArcGIS default Seed Radius is 5 pix

The Seed Radius determines how selective the Sselecting contiguous pixels. A larger Seed Radiu

APPL 41

Isla

The oIslanFinda mo

YING DATA TOOLS

nd Polygons

ther option on the Seed Tool Properties dialog is Include d Polygons. You should leave this option checked for use with Like Areas. For single feature mapping where you want to see re refined boundary, you may want to turn it off.

YSIS FOR ARCGIS

Preparing to use the Seed Tool1

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USING IMAGE ANAL42

Go through the following steps to activate the Seed Tool and generate a polygon in your image.

1. Open ArcCatalog and make sure your working directory appears in ArcCatalog, or navigate to it.

2. Click File, point to New, and click Shapefile. 3. Rename the New_Shapefile.4. Click the dropdown arrow and select Polygon.5. Check Show Details.6. Click Edit.

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7. Click Select, Import, or New to input the coordinate

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APPLYING DATA TOOLS

system the new shapefile will use. Clicking Import will allow you to import the coordinates of the image you are creating the shapefile for.

8. Click Apply and OK in the Spatial Reference Properties dialog.

9. Click OK in the Create New Shapefile dialog.10. Close ArcCatalog and click the dropdown arrow on the

Editor toolbar.11. Select Start Editing.

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Using the Seed Tool

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USING IMAGE ANAL44

These processes will take you through steps to change the Seed Radius and include Island Polygons. For an in-depth tutorial on using the Seed Tool and generating a polygon, see chapter 2 “Quick-start tutorial”.

Changing the Seed Radius

1. Click the Image Analysis dropdown arrow, and click Seed Tool Properties.

2. Type a new value in the Seed Radius text box.3. If you need to enable Include Island Polygons, check the

box.4. Click OK.

After growing the polygon in the image with the Seed Tool, go back to the Editor toolbar, click the dropdown arrow, and click Stop Editing.

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Image Info

og gives you the NoData. In order pixel in the image You will want to a of the image are ve to assign some you need to come e other pixels you 0 does contain mum value under ur NoData value ximum.

a will already be your image. This layed in the view uld be clear, or

o remember that you have no tion.

APPLYI

When aalter orbetter. you choimage sdesigna

You cathe entiapply Non the recalcuCurrenimportaBand isthat banrecalcusetting

The Imyou chodropdovalue thStatistiyou canarea ofMaximimage athe imaappliedautomakind ofto be co

NG DATA TOOLS

cs portion of the dialog also features a dropdown menu so designate the layer for which to calculate NoData. This

the dialog also names the Pixel Type and the Minimum and um values. When you click Recalc Stats, the statistics for the re recalculated using the NoData Value, and you can close ge in the view, then reopen it to see the NoData Value . The Representation Type area of the dialog will tically choose Continuous or Thematic depending on what image you have in your view. If you find that a file you need ntinuous is listed as thematic, you can change it here.

nalyzing images, you often have pixel values you need to manipulate in order to perceive different parts of the image The Image Info feature of Image Analysis for ArcGIS lets ose a NoData Value and recalculate the statistics for your o that a pixel value that is unimportant in your image can be ted as such.

n apply NoData to a single layer of your image instead of to re image if you want or need to do so. When you choose to oData to single layers, it is important that you click Apply

dialog before moving to the next layer. You can also late statistics (Recalc Stats) for single bands by choosing t Band in the Statistics box on the Image Info dialog. It is nt to remember that if you click Recalc Stats while Current selected, Image Info will only recalculate the statistics for d. If you want to set NoData for a single band, but

late statistics for all bands, you can choose All Bands after NoData in the single bands, and recalculate for all.

age Info dialog is found on the Image Analysis menu. When ose it, the images in your view will be displayed on a

wn menu under Layer Selection. You can then type the pixel at you wish to give the NoData pixels in your image. The

NoData Value

The NoDataValue section of the Image Info dialopportunity to label certain areas of your image asto do this, you assign a certain value that no other has to the pixels you want to classify as NoData.do this when the pixel values in that particular arenot important to your statistics or image. You hatype of value to those pixels to hold their place, soup with a value that's not being used for any of thwant to include. Using 0 does not work because value. Look at the Minimum value and the MaxiStatistics on the Image Info dialog and choose yoto be any number between the Minimum and Ma

Sometimes the pixel value you choose as NoDatused so that NoData matches some other part of problem becomes evident when the image is dispand there are black spots or triangles where it shoperhaps clear spots where it should be black. Alsyou can type N/A or leave the area blank so that NoData assigned if you don't want to use this op

YSIS FOR ARCGIS

Using the Image Info dialog

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USING IMAGE ANAL46

1. Click the Image Analysis dropdown arrow, and click Image Info.

2. Click the Layer Selection dropdown arrow to make sure the correct image is displayed.

3. Click the Statistics dropdown arrow to make sure the layer you want to recalculate is selected.

4. Choose All Bands or Current Band.5. Type the NoDataValue in the box.6. Make sure the correct Representation Type is chosen for

your image.7. Click Recalc Stats.8. Click Apply and OK.9. Close the image and re-open to view the results visually.

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Options

eme you want to Analysis extent. e as Display, As ver layer is active the area currently ed in on a portion on that portion of f the information

ed regardless of Specified below can also click the taset to use as the navigate to the le that has extents

APPLYI

You camenu. Tsetting a singlethey arWhen ythe OptUnion mosaicis recommosaiccheck tImage AnalysData Pr

The OpSize, andisplayclick thwant todirectoname indirectobutton you chobe saveframe. messagoperati

NG DATA TOOLS

ry to automatically come up every time you click the browse for an output image. The Analysis Coordinate System lets ose which coordinate system you would like the image to d with—the one for the input or the one for the active data Finally, you can select whether or not to have a warning e display if raster inputs have to be projected during analysis on.

in the view) are chosen. Same as Display refers todisplayed in the view. If the view has been zoomof a theme, then the functions would only operatethe theme. When you choose Same as Layer, all oin the Table of contents for that layer is considerwhether or not they are displayed in the view. Aslets you fill in the information for the extent. Youopen file button on the Extent tab to choose a daAnalysis extent. If you click this button, you candirectory where your data is stored and select a fifalling within the selected project area.

n access the Options dialog through the Image Analysis hrough this dialog, you can set an analysis mask as well as

the extent, cell size, and preferences for future operations or operation. It’s usually best to leave the options set at what

e, but there may be times you want or need to change them. ou’re mosaicking images, you can go to the Extent tab on ions dialog in order to set the extent at something other than of Inputs, which it automatically defaults to when king. The default extent is usually Intersection of Inputs. It

mended that you leave the default Union of Inputs when king, but you can change it. If you do so, you will need to he Use Extent from Analysis Options box on the Mosaic dialog. You can use the Options dialog with any Image is feature, but you may find it particularly useful with the eparation features that will be covered in the next chapter.

tions dialog has four tabs on it for General, Extent, Cell d Preferences. On the General tab, your output directory is

ed, and the Analysis mask will default to none, but if you e dropdown arrow, you can set it to any raster dataset. If you store your output images and shapefiles in one working ry, you can navigate to that directory or type the directory the Working directory box. This will allow your working

The Image Analysis Options dialog

Extent

The Extent tab lets you control how much of a thuse during processing. You do this by setting theThe rest of the tab will become active when SamSpecified below, and Same as Layer "......" (whate

YSIS FOR ARCGIS48

The oInterInterMosaarea PortifrommosaAnalhighlmosa

Whethe fithe dfieldextenmask

his is for the cell for ArcGIS. The

sis cell size. You uts, As Specified um of Inputs of the input files. meter image and .

ut that has the le, if you use eter image, the

nter whatever cell IS will adjust the

ayer in the view, yer.

r feet. To choose perties, and on the Units and choose

elds should not be properties are

USING IMAGE ANAL

The Extent tab on the Options dialog

one, click View in ArcMap, click Data Frame ProGeneral Tab, click the dropdown arrow for Map either Feet or Meters.

The Number of Rows and Number of Columns fiupdated manually as they will update as analysischanged.

ther options on the Analysis extent dropdown list are section of Inputs and Union of Inputs. When you choose section (which is the default extent for all functions except ic), Image Analysis for ArcGIS performs functions on the

of overlap common to the input images to the function. ons of the images outside the area of overlap are discounted analysis. Union is the default setting of Analysis extent for icking. When the extent is set to Union of Inputs, Image ysis for ArcGIS uses the union of every input theme. It is y recommended that you keep this default setting when icking images.

n you choose an extent that activates the rest of the Extent tab, elds are Top, Right, Bottom, and Left. If you are familiar with ata and want to enter exact coordinates, you can do so in these s. Same as Display and As Specified Below activate the Snap t to field where you can choose an image to snap the Analysis to.

Cell Size

The third tab on the Options dialog is Cell Size. Tsize of images you produce using Image Analysisfirst field on the tab is a dropdown list for Analycan choose Maximum of Inputs, Minimum of Inpbelow, or Same as Layer ".....". Choosing Maximyields an output that has the maximum resolutionFor example, if you use Image Difference on a 10a 20 meter image, the output is a 20 meter image

The Minimum of Inputs option produces an outpminimum resolution of the input files. For exampImage Difference on a 10 meter image and a 20 moutput is a 10 meter image.

When you choose As Specified below, you can esize you wish to use, and Image Analysis for ArcGoutput accordingly.

If you choose Same as Layer "....", indicating a lthe cell size reflects the current cell size of that la

The Cell Size field will display in either meters o

APPL 49

Pre

It is rdefauNeigchoicdata outpudata

The Noutpuclose

The Cdata outpu

alog

YING DATA TOOLS

file values of four pixels in a 2 × 2 window to calculate an t data file value by computing a weighted average of the input

file values with a bilinear function.

earest Neighbor option is a resampling method in which the t data file value is equal to the input pixel that has coordinates st to the retransformed coordinates of the output pixel.

ubic Convolution option is a resampling method that uses the file values of sixteen pixels in a 4 × 4 window to calculate an t data file value with a cubic function.

The Cell Size tab on the Options dialog

ferences

ecommended that you leave the preference choice to the lt of Bilinear Interpolation, but you can change it to Nearest

hbor or Cubic Convolution if your data requires one of those es. Bilinear Interpolation is a resampling method that uses the

The Preferences tab on the Options di

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Using the Options dialog

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USING IMAGE ANAL50

The following processes will take you through the parts you can change on the Options dialog.

The General Tab

1. Click the Image Analysis dropdown arrow, and click Options.

2. Navigate to the Working directory if it’s not displayed in the box.

3. Click the dropdown arrow and select the Analysis mask if you want one, or navigate to the directory where it is stored.

4. Choose the Analysis Coordinate System.5. Check or uncheck the Display warning box according to

your needs.6. Click the Extent tab to change Extents or OK to finish.

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The Extent Tab

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APPLYING DATA TOOLS

1. Click the dropdown arrow for Analysis extent, and choose an extent, or navigate to a directory to choose a dataset for the extent.

2. If the coordinate boxes are on, you can type in coordinates if you know the exact ones to use.

3. If activated, click the dropdown arrow, and choose an image to Snap extent to, or navigate to the directory where it is stored.

4. Click the Cell Size tab, or OK.

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Cell Size tab

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USING IMAGE ANAL52

1. Click the dropdown arrow, and choose the cell size, or navigate to the directory where it is stored.

2. If activated, type the cell size you want to use.3. Type the number of rows.4. Type the number of columns.5. Click the Preferences tab or OK.

The Preferences tab has only the one option of clicking the dropdown arrow and choosing to resample using either Nearest Neighbor, Bilinear Interpolation, or Cubic Convolution.

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ion 2

king with features

Sect

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4Usi

times necessary epare your data IS gives you to g data in Image

IN THIS C

• Create N

• Subset I

• Mosaic

• Reproje

4
ng Data Preparation
When using the Image Analysis for ArcGIS extension, it is someto prepare your data first. It is important to understand how to prbefore moving on to the different ways Image Analysis for ArcGmanipulate your data. You are given several options for preparinAnalysis for ArcGIS.

In this chapter you will learn how to:

• Create a new image• Subset an image• Mosaic images

• Reproject an image

HAPTER

ew Image

mage

Images

ct Image

YSIS FOR ARCGIS

Create New Image

many layers to

nitialize the new

into the fields, o close the dialog.

aximum Value

1

3

15

255

127

5,535

2,767

billion

56

The Crimage ffile as wthemat

ChoosecategorthemseThey ascale, s

Continquantitscale) abe mul(scanne

With throws (tchoosenumbe

USING IMAGE ANAL

The Number of Layers allows you to select how create in the new file.

The Initial Value lets you choose the number to ifile. Every cell is given this value.

When you are finished entering your informationyou can click OK to create the image, or Cancel t

eate New Image function makes it easy to create a new ile. It also allows you to define the size and content of the ell as choosing whether or not the new image type will be

ic or continuous.

thematic for raster layers that contain qualitative and ical information about an area. Thematic layers lend lves to applications in which categories or themes are used. re used to represent data measured on a nominal or ordinal uch as soils, land use, land cover, and roads.

uous data is represented in raster layers that contain ative (measuring a characteristic on an interval or ratio nd related, continuous values. Continuous raster layers can

tiband or single band such as Landsat, SPOT, digitized d) aerial photograph, DEM, slope, and temperature.

is feature, you also get to choose the value of columns and he default value is 512, but you can change that) and you the data type as well. The data type determines the type of rs and the range of values that can be stored in a raster layer.

Data Type Minimum Value

M

Unsigned 1 bit 0

Unsigned 2 bit 0

Unsigned 4 bit 0

Unsigned 8 bit 0

Signed 8 bit -128

Unsigned 16 bit 0 6

Signed 16 bit -32,768 3

Unsigned 32 bit

Signed 32 bit -2 billion 2

Float Single

57

Creating a new image

1

3

5

2

USING DATA PREPARATION

1. Click the Image Analysis dropdown arrow, point to Data Preparation, and click Create New Image.

2. Navigate to the directory where the Output Image should be stored.

3. Choose Thematic or Continuous as the Output Image Type.

4. Type or click the arrows to enter how many Columns or Rows if different from the default number of 512.

5. Click the dropdown arrow to choose the Data Type.6. Type or click the arrows to enter Number of Layers.7. Type or click the arrows to enter the Initial Value.

8. Click OK.

7

8

4

6

YSIS FOR ARCGIS

Subset Image

ting

58

This fudata filan imagto studyextranebe imp

The Suseparata TM imsubset

The SuspatiallfrequenOptionthe cellArcGISparticusubset a rectanthere. Iin the Sextract

Followunderg

This fe

USING IMAGE ANAL

from the image.

ing are illustrations of a TM image of the Amazon as it oes a spectral subset.

ature is also accessible from the Utilities menu.

Amazon TM after a spectral subset

nction allows you to copy a portion (a subset) of an input e into an output data file. This may be necessary if you have e file that is much larger than the particular area you need . Subset Image has the advantage of not only eliminating

ous data, but it also speeds up processing as well, which can ortant when dealing with multiband data.

bset Image function works on multiband continuous data to e that data into bands. For example, if you are working with

age that has seven bands of data, you may wish to make a of bands 2, 3, and 4, and discard the rest.

bset Image function can be used to subset an image either y or spectrally. You will probably spatially subset more tly than spectrally. To subset spatially, you first bring up the

s dialog, which allows you to apply a mask or extent or set size. These options are used for all Image Analysis for functions including Subset Image. Spatial subsets are

larly useful if you have a large image and you only want to part of it for analysis. You can use the Zoom In tool to draw gle around the specific area you wish to subset and go from

f you wish to subset an image spectrally, you do it directly ubset Image dialog by entering the desired band numbers to

The Amazon TM image before subset

USIN 59

The noptio

In orIn toselecby Tmeascoordsubse

Extent in Options

G DATA PREPARATION

der to specify the particular area to subset, you click the Zoom ol, draw a rectangle over the area, open the options dialog, and t Same As Display on the Extent tab. The rectangle is defined op, Left, Bottom, and Right coordinates. Top and Bottom are ured as the locations on the Y-axis and the Left and Right inates are measured on the X-axis. You can then save the t image and work from there on your analysis.

The Pentagon subset image after setting the Analysis

ext illustrations reflect images using the spatial subsetting n.

The image of the Pentagon before spatial subsetting

The Options dialog

YSIS FOR ARCGIS

Subsetting an image spectral ly

5

USING IMAGE ANAL60

1. Click Add Data to add the image to the view.2. Double-click the image name in the Table of contents to

open Layer Properties.3. Click the Symbology tab in Layer Properties.4. Click Stretched in the Show panel.5. Click the Band dropdown arrow, and select the layer you

want to subset.6. Click Apply and OK.

1

6

34

61

7. Click the Image Analysis dropdown arrow, point to Data

10

8

7


Preparation, and click Subset Image.8. Click the Input Image dropdown arrow, and click the file

you want to use, or navigate to the directory where it is stored.

9. Using a comma for separation, type the band numbers you want to subset in the text box.

10. Type the file name of the Output Image, or navigate to the directory where it should be stored.

11. Click OK.

9

11

YSIS FOR ARCGIS

Subsetting an image spatial ly

5

6

USING IMAGE ANAL62

1. Click the Add Data button to add your image.2. Click the Zoom In tool, and draw a rectangle over the

area you want to subset.3. Click the Image Analysis menu, and click Options.4. Click the Extent tab.5. Click the Analysis extent dropdown arrow, and select

Same As Display.6. Click Apply and OK.7. Click the Image Analysis dropdown arrow and click Save

As, and save the image in the appropriate directory.

3

7

4

2

1

63

Mosaic Imagesferences tab. For earest Neighbor.

t differ in their ling methods use e an edge effect.

d using whatever ialog. During kup table, and the and should be the stretch of each ll color balance.

of how to handle ximum value,

rlap area with the .

l in the overlap ls in the

erlap area by the erlapping images.

rlap area with the in the overlapping

USING D

Mosaicto formprojectprojectalso sulayers. themat

It is extin the vmosaicbased olarge n

It is alssame nwith a ssubset the num

You caWhen tAnalysto the m4-metemosaicSize inoutput

The Exfor mosdifferenthe Usedialog.of Inpu

ATA PREPARATION

aximum cell size so if you mosaic two images, one with a r resolution and one with a 5-meter resolution, the output ked image has a 5-meter resolution. You can set the Cell the Options dialog to whatever cell size you like so that the mosaicked image has the cell size you selected.

tent tab on the Options dialog will default to Union of Inputs aicking images. If, for some reason, you want to use a t extent, you can change it in the Options dialog and check Extent from Analysis Options box on the Mosaic Images

It is recommended that you leave it at the default of Union ts.

overlapping images.

Minimum Value — replaces each pixel of the ovlesser value of the corresponding pixels in the ov

Average Value — replaces each pixel in the oveaverage of the values of the corresponding pixels images.

king is the process of joining georeferenced images together a larger image. The input images must all contain map and ion information, although they need not be in the same ion or have the same cell sizes. Calibrated input images are pported. All input images must have the same number of You can mosaic single or multiband continuous data, or ic data.

remely important when mosaicking to arrange your images iew as you want the output theme to appear before you them. Image Analysis for ArcGIS mosaics images strictly n their appearance in the view. This allows you to mosaic a

umber of images without having to make them all active.

o important that the images you plan to mosaic contain the umber of bands. You cannot mosaic a seven band TM image ix band TM image. You can, however, use Subset Image to

bands from an existing image and then mosaic regardless of ber of bands they originally contained.

n mosaic images with different cell sizes or resolutions. his happens you can consult the settings in the Image is Options dialog for Cell Size. The Cell Size is initially set

Another Options feature to take note of is the Premosaicking images, you should resample using NThis will ensure that the mosaicked pixels do noappearance from the original image. Other resampaverages to compute pixel values and can produc

When you apply Mosaic, the images are processestretch you’ve specified in the Layer Properties dprocessing, each image is fed through its own loooutput mosaicked image has the stretch built in, viewed with no stretch. This allows you to adjust image independently to achieve the desired overa

With the Mosaic tool you are also given a choiceimage overlaps by using the order displayed, maminimum value, or average value.

Choose:

Order Displayed — replaces each pixel in the ovepixel value of the image that is on top in the view

Maximum Value — in order to replace each pixearea with the greater value of corresponding pixe

YSIS FOR ARCGIS64

The cbrighbrighutilizSymare athe v

USING IMAGE ANAL

olor balancing options let you choose between balancing by tness/contrast, histogram matching, or none. If you choose tness/contrast, the mosaicked image will be balanced by ing the adjustments you have made in Layer Properties/ bology. If you choose Histogram Matching, the input images djusted to have similar histograms to the top of the image in iew. Select None if you don’t want the pixel values adjusted.

65

How to Mosaic Images

2

3

5

7


1. Add the images you want to mosaic to the view.2. Arrange images in the view in the order that you want

them in the mosaic.3. Click the Image Analysis dropdown arrow, point to Data

Preparation, and click Mosaic Images.4. Click the Handle Image Overlaps by dropdown arrow,

and click the method you want to use.5. If you want the images automatically cropped, check the

box, and enter the Percent by which to crop the images.6. Choose the Color Balance method.7. Check the box if you want to use the extent you set in

Analysis Options.8. Navigate to the directory where the Output Image should

be stored.9. Click OK.

For more information on mosaicking images, see chapter 2 “Quick-start tutorial’’.

4

6

8

YSIS FOR ARCGIS

Reproject Imageoordinate System

66

Reprojfrom onImage OptionSize, an

ArcMasetting Propertprojectsystemfor Arc

At timeBy havPropertare the

USING IMAGE ANAL

Before Reproject Image

ect Image gives you the ability to reproject raster image data e map projection to another. Reproject Image, like all

Analysis for ArcGIS functions, observes the settings in the s dialog so don’t forget to use Options to set Extent, Cell d so on if so desired.

p has the capability to reproject images on the fly by simply the desired projection and choosing View/Data Frame ies and selecting the Coordinate System tab. The desired ion may then be selected. After you select the coordinate , you apply it and go to Reproject Image n Image Analysis GIS.

s you may need to produce an image in a specific projection. ing the desired output projection specified in the Data Frame ies, the only things you need to specify in Reproject Image input and output images.

Here is the reprojected image after changing the Cto Mercator (world):

After Reproject Image

67

How to Reproject an Image


1. Click Add Data, and add the image you want to reproject to the view.

2. Right-click in the view, and click on Properties to bring up the Data Frame Properties dialog.

3. Click on the Coordinate System tab.4. Click Predefined and choose whatever coordinate

system you want to use to reproject the image.5. Click Apply and OK.

2

4

1

5

3

YSIS FOR ARCGIS

6. Click the Image Analysis dropdown arrow, point to Data

6

7

8

USING IMAGE ANAL68

Preparation, and click Reproject Image.7. Click the Input Image dropdown arrow and click the file

you want to use, or navigate to the directory where it is stored.


9. Click OK.

9

69

1Performing Spatial Enhancement

Spatial Enhancement is a function that enhances an image using the values of individual and surrounding pixels. Spatial Enhancement deals largely with spatial frequency, which is the difference between the highest and lowest values of a contiguous set of pixels. Jensen (1986) defines spatial frequency as “the number of changes in brightness value per unit distance for any part of an image.”

There are three types of spatial frequency:

• zero spatial frequency — a flat image, in which every pixel has the same value• low spatial frequency — an image consisting of a smoothly varying gray scale• high spatial frequency — an image consisting of drastically changing pixel

values such as a checkerboard of black and white pixelsThe Spatial Enhancement feature lets you use convolution, non-directional edge, focal analysis, and resolution merge to enhance your images. Depending on what you need to do to your image, you will select one feature from the Spatial Enhancement menu. This chapter will focus on the explanation of these features as well as how to apply them to your data.

This chapter is organized according to the order in which the Spatial Enhancement tools appear. You may want to skip ahead if the information you are seeking is about one of the tools near the end of the menu list.

IN THIS CHAPTER

• Convolution

• Non-Directional Edge

• Focal Analysis

• Resolution Merge

5

YSIS FOR ARCGIS

Convolut ion

value in the el value that d the total is shown in this

-1

-1

-1

70

Convolacross frequen

A convthe valunumbeparticubecaus

Appl

Apply dropdoEnhancrefers tenhancspatial

Conv

To undconvol(in onewindow

USING IMAGE ANAL

band) so that the pixel to be convolved is in the center of the . To compute the output value for this pixel, each

convolution kernel is multiplied by the image pixcorresponds to it. These products are summed, andivided by the sum of the values in the kernel, asequation:

integer [((-1 × 8) + (-1 × 6) + (-1 × 6) + (-1 × 2) + (16 × 8) + (-1 × 6) + (-1 × 2) + (-1 × 2) + (-1 × 8))/ : (-1 + -1 + -1 + -1 + 16 + -1 + -1 + -1 + -1)]= int [(128-40) / (16-8)] = int (88 / 8) = int (11) = 11

ution filtering is the process of averaging small sets of pixels an image. Convolution filtering is used to change the spatial cy characteristics of an image (Jensen 1996).

olution kernel is a matrix of numbers that is used to average e of each pixel with the values of surrounding pixels. The

rs in the matrix serve to weight this average toward lar pixels. These numbers are often called coefficients, e they are used as such in the mathematical equations.

ying convolution f i l tering

Convolution filtering by clicking the Image Analysis wn arrow, and choosing Convolution from the Spatial ement menu. The word filtering is a broad term, which o the altering of spatial or spectral features for image ement (Jensen 1996). Convolution filtering is one method of filtering. Some texts use the terms synonymously.

olution example

erstand how one pixel is convolved, imagine that the ution kernel is overlaid on the data file values of the image

2 8 6 6 6

2 8 6 6 6

2 2 8 6 6

2 2 2 8 6

2 2 2 2 8

Kernel

-1 -1

-1 16

-1 -1

Data

PERF 71

Wheconv

The krelatibeco

Con

The fthe p

tion kernel at

at corresponds to

ssuming a square 3 × 3)

ents of the kernel, s is zero

rdt 1983

ominator of the latively the same ero (division by o.

ORMING SPATIAL ENHANCEMENT

ollowing formula is used to derive an output data file value for ixel being convolved (in the center):

or 1 if the sum of coefficient

V = the output pixel value

Source: Modified from Jensen 1996; Schowenge

The sum of the coefficients (F) is used as the denequation above, so that the output values are in rerange as the input values. Since F cannot equal zzero is not defined), F is set to 1 if the sum is zer

n the 2 × 2 set of pixels near the center of this 5 × 5 image is olved, the output values are:

ernel used in this example is a high frequency kernel. The vely lower values become lower, and the higher values me higher, thus increasing the spatial frequency of the image.

volution formula

1 2 3 4 5

1 - - - - -

2 - 11 5 - -

3 - 0 11 - -

4 - - - - -

5 - - - - -

Where:

fij = the coefficient of a convoluposition i,j (in the kernel)

dij = the data value of the pixel thfij

q = the dimension of the kernel, akernel (if q = 3, the kernel is

F = either the sum of the coeffici

V

fijdij

j 1=

q

∑

i 1=

q

∑

F-----------------------------------=

YSIS FOR ARCGIS72

Zer

Zerothe ksum abovby ze

This

• z• l• e

m

Thersmooa shaedgefrequonly

Zerodirec(Jens

the effect of

since they bring xels. Unlike edge ht edges and do

pixels in which a es, like this...

igh frequency ely high value is

-1

-1

-1

FTER

- -

10 -

- -

USING IMAGE ANAL

tion. For example, this 3 × 3 kernel is biased to the south en 1996).

-1 -1 -1

1 -2 1

1 1 1...the low value gets lower. Inversely, when the hkernel is used on a set of pixels in which a relativsurrounded by lower values...

BEFORE A

204 200 197 -

201 106 209 -

198 200 210 -

o sum kernels

sum kernels are kernels in which the sum of all coefficients in ernel equals zero. When a zero sum kernel is used, then the of the coefficients is not used in the convolution equation, as e. In this case, no division is performed (F = 1), since division ro is not defined.

generally causes the output values to be:

ero in areas where all input values are equal (no edges) ow in areas of low spatial frequency xtreme in areas of high spatial frequency (high values become uch higher, low values become much lower)

efore, a zero sum kernel is an edge detector, which usually ths out or zeros out areas of low spatial frequency and creates rp contrast where spatial frequency is high, which is at the s between homogeneous (homogeneity is low spatial ency) groups of pixels. The resulting image often consists of edges and zeros.

sum kernels can be biased to detect edges in a particular

High frequency kernels

A high frequency kernel, or high pass kernel, hasincreasing spatial frequency.

High frequency kernels serve as edge enhancers,out the edges between homogeneous groups of pidetectors (such as zero sum kernels), they highlignot necessarily eliminate other features.

When a high frequency kernel is used on a set ofrelatively low value is surrounded by higher valu

-1 -1

-1 16

-1 -1

PERF 73

...theincre

Low

Belowhic

This to besmoo

ORMING SPATIAL ENHANCEMENT

kernel simply averages the values of the pixels, causing them more homogeneous. The resulting image looks either more th or more blurred.

1 1 1

high value becomes higher. In either case, spatial frequency is ased by this kernel.

frequency kernels

w is an example of a low frequency kernel, or low pass kernel, h decreases spatial frequency.

BEFORE AFTER

64 60 57 - - -

61 125 69 - 188 -

58 60 70 - - -

1 1 1

1 1 1

Convolution With High Pass

Convolution with High Pass

YSIS FOR ARCGIS

Apply Convolution

1

3

4

5

USING IMAGE ANAL74

1. Click the Image Analysis dropdown arrow, point to Spatial Enhancement, and click Convolution.

2. Click the Input Image dropdown arrow, and click a file, or navigate to the directory where the file is stored.

3. Click the Kernel dropdown arrow, and click the kernel you want to use.

4. Choose Reflection or Background Fill.5. Navigate to the directory where the Output Image should

be stored. 6. Click OK.

6

2

Applying Convolut ion

Reflection fills in the area beyond the edge of the of the image with a reflection of the values at the edge. Background fill uses zeros to fill in the kernel area beyond the edge of the image.

Convolution allows you to perform image enhancement operations such as averaging and high pass or low pass filtering.

Each data file value of the new output file is calculated by centering the kernel over a pixel and multiplying the original values of the center pixel and the appropriate surrounding pixels by the corresponding coefficients from the matrix. To make sure the output values are within the general range of the input values, these numbers are summed and then divided by the sum of the coefficients. If the sum is zero, the division is not performed.

75

Non-Direct ional Edge

onal Edge

PERFOR

The NoorthogoSobel acalculadirectiothe orig

The Noconvolare impconvoldetectio

For thismodel accordi

Sobel

Prewit

MING SPATIAL ENHANCEMENT

1 0 1–

1 0 1–

1 0 1–

vertical

1– 1– 1–

0 0 01 1 1horizontal

t=

After Non-Directional Edge

n-Directional Edge function averages the results of two nal first derivative edge detectors. The filters used are the nd Prewitt filters. Both of these filters are based on a tion of the 1st derivative, or slope, in both the x and y ns. Both use orthogonal kernels convolved separately with inal image, and then combined.

n-Directional Edge is based on the Sobel zero-sum ution kernel. Most of the standard image processing filters lemented as a single pass moving window (kernel)

ution. Examples include low pass, edge enhance, edge n, and summary filters.

model, a Sobel filter has been selected. To convert this to the Prewitt filter calculation, the kernels must be changed ng to the example below.

1 0 1–

2 0 2–

1 0 1–

vertical

1– 2– 1–

0 0 01 2 1horizontal

=

Image of Seattle before applying Non-Directi

YSIS FOR ARCGIS

Using Non-Directional Edge

1

3

4

5

USING IMAGE ANAL76

1. Click the Image Analysis dropdown arrow, point to Spatial Enhancement, and click Non-Directional Edge.


3. Choose Sobel or Prewitt.4. Choose Reflection or Background Fill.5. Type the file name of the Output Image, or navigate to

the directory where it should be stored.6. Click OK.

6

2

Using Non-Direct ional Edge

In step 4, reflection fills in the area beyond the edge of the image with a reflection of the values at the edge. Background fill uses zeros to fill in the kernel area beyond the edge of the image.

77

Focal Analysis

PERFOR

The Fotypes osimilar

This mrandomimperfethemat

Focal Ainterestpixel o

• Sta• Su• Me• Me• Mi• Ma

These fregion


After Focal Analysis is performed

cal Analysis function enables you to perform one of several f analysis on class values in an image file using a process to convolution filtering.

odel (Median Filter) is useful for reducing noise such as spikes in data sets, dead sensor striping, and other impulse ctions in any type of image. It is also useful for enhancing

ic images.

nalysis evaluates the region surrounding the pixel of (center pixel). The operations that can be performed on the f interest include:

ndard Deviation — measure of textureman — good for despeckling radar datadian — despeckle radarnx

unctions allow you to select the size of the surrounding to evaluate by selecting the window size.

An image before Focal Analysis

YSIS FOR ARCGIS

Applying Focal Analysis

1

3

4

6

USING IMAGE ANAL78

1. Click the Image Analysis dropdown arrow, point to Spatial Enhancement, and click Focal.


3. Click the Focal Function dropdown arrow, and click the function you want to use.

4. Click the Neighborhood Shape dropdown arrow, and click the shape you want to use.

5. Click the Neighborhood Definition dropdown arrow, and click the Matrix size you want to use.

6. Type the file name of the Output Image, or navigate to the directory where it should be stored.

7. Click OK.

7

2

5

Focal Analysis Resul ts

Focal Analysis is similar to Convolution in the process that it uses. With Focal Analysis, you are able to perform several different types of analysis on the pixel values in an image file.

79

Resolut ion Merge

cording to the

B3] ×

B3] ×

B3] ×

increase contrast (i.e., to provide eas such as urban g RGB images

igh ends of the aling images.

ce RGB images, the input

SPOT or Landsat he resulting

ds 1, 2, 3 to RGB.

PERFOR

The resspectradifferin

Landsa28.5 mspatial seven-bcharact

A nummerge.transfopanchrbands (

Chavezcompo

In the acompopanchrcontaincover tdoes noband (T

Anothehigh fre(i.e., SPTM im

The Reof resamresolut


he full spectral range that TM data does, this assumption t strictly hold. It is unacceptable to resample the thermal M6) based on the visible (SPOT panchromatic) image.

r technique (Schowengerdt 1980) additively combines a quency image derived from the high spatial resolution data OT panchromatic) with the high spectral resolution Landsat

age.

solution Merge function uses the Brovey Transform method pling low spatial resolution data to a higher spatial

ion while retaining spectral information:

Since the Brovey Transform is intended to produonly three bands at a time should be merged frommultispectral scene, such as bands 3, 2, 1 from a TM image or 4, 3, 2 from a Landsat TM image. Tmerged image should then be displayed with ban

olution of a specific sensor can refer to radiometric, spatial, l, or temporal resolution. This function merges imagery of g spatial resolutions.

t TM sensors have seven bands with a spatial resolution of . SPOT panchromatic has one broad band with very good resolution—10 m. Combining these two images to yield a and data set with 10 m resolution provides the best eristics of both sensors.

ber of models have been suggested to achieve this image Welch and Ehlers (1987) used forward-reverse RGB to IHS rms, replacing I (from transformed TM data) with the SPOT omatic image. However, this technique is limited to three R,G,B).

(1991), among others, uses the forward-reverse principal nents transforms with the SPOT image, replacing PC-1.

bove two techniques, it is assumed that the intensity nent (PC-1 or I) is spectrally equivalent to the SPOT omatic image, and that all the spectral information is ed in the other PCs or in H and S. Since SPOT data does not

Brovey Transform

In the Brovey Transform, three bands are used acfollowing formula:

DNB1_new = [DNB1 / DNB1 + DNB2 + DN[DNhigh res. image]



Where:

B = band

The Brovey Transform was developed to visuallyin the low and high ends of an image’s histogramcontrast in shadows, water and high reflectance arfeatures). Brovey Transform is good for producinwith a higher degree of contrast in the low and himage histogram and for producing visually appe

YSIS FOR ARCGIS

Resolution Merge

1

3

4

USING IMAGE ANAL80

1. Click the Image Analysis dropdown arrow, point to Spatial Enhancement, and click Resolution Merge.

2. Click the High Resolution Image dropdown arrow, and click a file, or navigate to the directory where the file is stored.

3. Click the Multi-Spectral Image dropdown arrow, and click a file, or navigate to the directory where the file is stored.


5. Click OK.

5

2

Using Resolut ion Merge

Use Resolution Merge to integrate imagery of different spatial resolutions (pixel size).

81

age

PERFOR

The fol


lowing images display the Resolution Merge function:

High Resolution Image Multi-Spectral Im

Resolution Merge

YSIS FOR ARCGIS
USING IMAGE ANAL82

83

1Usi

pixels in an unt the values of

image by using e points and the

plied to one band enhancement of endent, single-

IN THIS C

• LUT (Lo

• Histogra

• Histogra

• Brightne

6
ng Radiometric Enhancement
Radiometric enhancement deals with the individual values of theimage. It differs from Spatial Enhancement, which takes into acconeighboring pixels.

Radiometric Enhancement consists of functions to enhance your the values of individual pixels within each band. Depending on thbands in which they appear, radiometric enhancements that are apmay not be appropriate for other bands. Therefore, the radiometrica multiband image can usually be considered as a series of indepband enhancements (Faust 1989).

HAPTER

okup Table) Stretch

m Equalization

m Matching

ss Inversion

YSIS FOR ARCGIS

LUT Stretch

radually increase ying the same e. Usually, one range while

h

enhancement of a ble into three create a number e. You can in a single color nhancing image

ws two rules:

e no break in the ge specifications the data value

upward,

rcentage of the ies. Since rules 1 htness values are ess of other

nge increases, it

84

LUT Sas mod

Cont

When rdevice,is illust

Contrastretchiwider rAnalys

Linea

The terenhancperformpolylindifferen

Linea

A lineacontrasimage d

In mosusuallydisplaythe disp

USING IMAGE ANAL

r contrast stretch is a simple way to improve the visible t of an image. It is often necessary to contrast-stretch raw ata, so that they can be seen on the display.

t raw data, the data file values fall within a narrow range— a range much narrower than the display device is capable of ing. That range can be expanded to utilize the total range of lay device (usually 0 to 255).

range.2. The data values specified can go only in an

increasing direction.

The contrast value for each range represents a peavailable output range that particular range occupand 2 above are enforced, as the contrast and brigchanged, they may affect the contrast and brightnranges. For example, if the contrast of the low raforces the contrast of the middle to decrease.

tretch creates an output image that contains the data values ified by a lookup table. The output is 3 bands.

rast stretch

adiometric enhancements are performed on the display the transformation of data file values into brightness values rated by the graph of a lookup table.

st stretching involves taking a narrow input range and ng the output brightness values for those same pixels over a ange. This process is done in Layer Properties in Image is for ArcGIS.

r and nonlinear

ms linear and nonlinear, when describing types of spectral ement, refer to the function that is applied to the data to the enhancement. A piecewise linear stretch uses a

e function to increase contrast to varying degrees over t ranges of the data.

r contrast stretch

Nonlinear contrast stretch

A nonlinear spectral enhancement can be used to gor decrease contrast over a range, instead of applamount of contrast (slope) across the entire imagnonlinear enhancements bring out the contrast indecreasing the contrast in other ranges.

Piecewise l inear contrast stretc

A piecewise linear contrast stretch allows for the specific portion of data by dividing the lookup tasections: low, middle, and high. It enables you toof straight line segments that can simulate a curvenhance the contrast or brightness of any sectiongun at a time. This technique is very useful for eareas in shadow or other areas of low contrast.

A piecewise linear contrast stretch normally follo

1. The data values are continuous; there can bvalues between High, Middle, and Low. Ranadjust in relation to any changes to maintain

USIN 85

Con

Usuaso thcreatrangestretcThesthe n

The sand odeviatransspecibe ustwo sdata appro

The mand mdata notabshaddata the m

Var

Therchanamouillustbe br

ulates the e areas and le of a piecewise breakpoints to the

G RADIOMETRIC ENHANCEMENT

le exception occurs when the feature being sought is in ow. The shadow pixels are usually at the low extreme of the file values, outside the range of two standard deviations from ean.

ying the contrast stretch

e are variations of the contrast stretch that can be used to ge the contrast of values over a specific range, or by a specific nt. By manipulating the lookup tables as in the following ration, the maximum contrast in the features of an image can ought out.

trast stretch on the display

lly, a contrast stretch is performed on the display device only, at the data file values are not changed. Lookup tables are ed that convert the range of data file values to the maximum of the display device. You can then edit and save the contrast h values and lookup tables as part of the raster data image file. e values are loaded into the view as the default display values ext time the image is displayed.

tatistics in the image file contain the mean, standard deviation, ther statistics on each band of data. The mean and standard tion are used to determine the range of data file values to be lated into brightness values or new data file values. You can fy the number of standard deviations from the mean that are to ed in the contrast stretch. Usually the data file values that are tandard deviations above and below the mean are used. If the has a normal distribution, then this range represents ximately 95 percent of the data.

ean and standard deviation are used instead of the minimum aximum data file values because the minimum and maximum

file values are usually not representative of most of the data. A

This figure shows how the contrast stretch maniphistogram of the data, increasing contrast in somdecreasing it in others. This is also a good examplinear contrast stretch, which is created by adding histogram.

YSIS FOR ARCGIS

Apply LUT Stretch Class

1

3

USING IMAGE ANAL86

1. Click the Image Analysis dropdown arrow, point to Radiometric Enhancement, and click LUT Stretch.

2. Click the Input Image dropdown arrow, and click the file you want to use, or navigate to the directory where it is stored.

3. Navigate to the directory where the Output Image should be stored. Set the output type to TIFF.

4. Click OK.

4

2

LUT Stretch Class

LUT Stretch Class provides a means of producing an output image that has the stretch built into the pixel values to use with packages that have no stretching capabilities.

87

Histogram Equal izat ionalues of an image eassigned to a red sets of pixels. the bins to which

ber of bins, in the following

the image

els per bin

s, so that the ossible. Consider

. To equalize this

USING R

Histogrpixel vpixels whistogrhistogr

Histogrgroupshave th

ADIOMETRIC ENHANCEMENT

pixels at peak are spreadapart - contrast is gained

pixels attail aregrouped -contrastis lost

the following:

There are 240 pixels represented by this histogramhistogram to 10 bins, there would be:

240 pixels / 10 bins = 24 pixels per bin = A

am Equalization is a nonlinear stretch that redistributes alues so that there is approximately the same number of

ith each value within a range. The result approximates a flat am. Therefore, contrast is increased at the peaks of the am and lessened at the tails.

am Equalization can also separate pixels into distinct if there are few output values over a wide range. This can e visual effect of a crude classification.

Original Histogram

After Equalization

peak

tail

To perform a Histogram Equalization, the pixel v(either data file values or brightness values) are rcertain number of bins, which are simply numbeThe pixels are then given new values, based uponthey are assigned.

The total number of pixels is divided by the numequaling the number of pixels per bin, as shown equation:

Where:

N = the number of bins

T = the total number of pixels in

A = the equalized number of pix

The pixels of each input value are assigned to binnumber of pixels in each bin is as close to A as p

A TN----=

YSIS FOR ARCGIS88

To as

Whe

AH

in

B

7

his example, M = that the equalized tput histogram of stration:

ple data with the gains contrast in the input range of ata values at the er. Input values 0 ast among the tail ightest regions of

A = 24

USING IMAGE ANAL

re:

= equalized number of pixels per bin (see above)

i = the number of values with the value i (histogram)

t = integer function (truncating real numbers to integer)

i = bin number for pixels with value i

A

Effect on contrast

By comparing the original histogram of the examone above, you can see that the enhanced image the peaks of the original histogram. For example,3 to 7 is stretched to the range 1 to 8. However, dtails of the original histogram are grouped togeththrough 2 all have the output value of 0. So, contrpixels, which usually make up the darkest and brthe input image, is lost.

0 1 2 3 4 5 6 7 8 9

output data file values

sign pixels to bins, the following equation is used:

0 1 2 3 4 5 6 7 8 9

5 5

10

15

60 60

40

30

10

5

num

ber

of p

ixel

s

data file values

A = 24

Bi int

Hk

k 1=

i 1–

∑ Hi

2-----+

----------------------------------=

Source: Modified from Gonzalez and Wintz 197

The 10 bins are rescaled to the range 0 to M. In t9, because the input values ranged from 0 to 9, so histogram can be compared to the original. The outhis equalized image looks like the following illu

15

60 60

40

30

num

ber

of p

ixel

s

2015

0

1

2

3

4 5

78

9

6

numbers inside bars are input data file values

0 0 0

USIN 89

The rrarelSets bins.

G RADIOMETRIC ENHANCEMENT

esulting histogram is not exactly flat, since the pixels can y be grouped together into bins with an equal number of pixels. of pixels with the same value are never split up to form equal

YSIS FOR ARCGIS

Performing Histogram Equalization

1

3

4

USING IMAGE ANAL90

1. Click the Image Analysis dropdown arrow, point to Radiometric Enhancement, and click Histogram Equalization.


3. Type or click the arrows to enter the Number of Bins.4. Navigate to the directory where the Output Image should


5

2

Histogram Equal izat ion

Perform Histogram Equalization when you need to redistribute pixels to approximate a flat histogram.

The Histogram Equalization process works by redistributing pixel values so that there are approximately the same number of pixels with each value within a range.

Histogram Equalization can also separate pixels into distinct groups if there are few output values over a wide range. This process can have the effect of a crude classification.

91

Histogram Matching

up table (b),

input255

(b)

USING R

Histogrthat conhistogrdata ofdays, oeffects.detectio

To achimages

• Th• Re

sam• Fo

be• Th

sam

To matwhich sother, a


am Matching is the process of determining a lookup table verts the histogram of one image so that it resembles the

am of another. Histogram Matching is useful for matching the same or adjacent scenes that were collected on separate r are slightly different because of sun angle or atmospheric This is especially useful for mosaicking or change n.

ieve good results with Histogram Matching, the two input should have similar characteristics:

e general shape of the histogram curves should be similar. lative dark and light features in the image should be the

e. r some applications, the spatial resolution of the data should the same. e relative distributions of land covers should be about the

e, even when matching scenes that are not of the same area.

ch the histograms, a lookup table is mathematically derived, erves as a function for converting one histogram to the s illustrated here.

Source histogram (a), mapped through the look

approximates model histogram (c).

freq

uenc

y

input0 255

freq

uenc

y

0

freq

uenc

y

input0 255

+

=

(a)

(c)

YSIS FOR ARCGIS

Performing Histogram Matching

1

3

4

USING IMAGE ANAL92

1. Click the Image Analysis dropdown arrow, point to Radiometric Enhancement, and click Histogram Match.


3. Click the Match Image dropdown arrow, and click the file you want to use, or navigate to the directory where it is stored.


5. Click OK.

5

2

Histogram Matching

Perform Histogram Matching when using matching data of the same or adjacent scenes that were gathered on different days and have differences due to the angle of the sun or atmospheric effects

Histogram Matching mathematically determines a lookup table that will convert the histogram of one image to resemble the histogram of another, and is particularly useful for mosaicking images or change detection.

93

Brightness Inversion

sion

USING R

The Broppositand lignegativ

Inversein the dfollowi

DN

DN


An image before Brightness Inversion

ightness Inversion functions produce images that have the e contrast of the original image. Dark detail becomes light, ht detail becomes dark. This can also be used to invert a e image that has been scanned to produce a positive image.

is useful for emphasizing detail that would otherwise be lost arkness of the low DN pixels. This function applies the ng algorithm:

out = 1.0 if 0.0 < DNin < 0.1

out = 0.1DNin

if 0.1 < DN < 1

The same image after Brightness Inver

YSIS FOR ARCGIS

Applying Brightness Inversion

1

3

USING IMAGE ANAL94

1. Click the Image Analysis dropdown arrow, point to Radiometric Enhancement, and click Brightness Inversion.



4. Click OK.

4

2

Brightness Inversion

This function allows both linear and nonlinear reversal of the image intensity range. Images can be produced that have the opposite contrast of the original image. Dark detail becomes light, and light becomes dark

95

1App

es of each pixel re than one band

or guns (R, G, B)atterns as might ore natural state image from red,

IN THIS C

• RGB to

• IHS to R

• Vegetati

• Color IR

7
lying Spectral Enhancement
Spectral Enhancement enhances images by transforming the valuon a multiband basis. The techniques in this chapter all require moof data. They can be used to:

• extract new bands of data that are more interpretable to the eye• apply mathematical transforms and algorithms• display a wider variety of information in the three available colYou can use the features of Spectral Enhancement to study such poccur with deforestation or crop rotation and to see images in a mor view images in different ways, such as changing the bands in angreen, and blue to intensity, hue, and saturation.

HAPTER

IHS

GB

ve Indices

to Natural Color

YSIS FOR ARCGIS

RGB to IHS

to IHS

B to IHS transform

intensity

96

The cosystemblue (Rbands oR,G,B

Howevintensitparamethat it p

• Intva

• Safro

• HuthebluIn beto

USING IMAGE ANAL

The algorithm used in the Image Analysis for ArcGIS RG(Conrac 1980)

R M r–M m–---------------=

G M g–M m–---------------=

B M b–M m–---------------=

lor monitors used for image display on image processing s have three color guns. These correspond to red, green, and ,G,B), the additive primary colors. When displaying three f a multiband data set, the viewed image is said to be in

space.

er, it is possible to define an alternate color space that uses y (I), hue (H), and saturation (S) as the three positioned ters (in lieu of R, G, and B). This system is advantageous in resents colors more nearly as perceived by the human eye.

ensity is the overall brightness of the scene (like PC-1) and ries from 0 (black) to 1 (white). turation represents the purity of color and also varies linearly m 0 to 1. e is representative of the color or dominant wavelength of pixel. It varies from 0 at the red midpoint through green and e back to the red midpoint at 360. It is a circular dimension. the following image, 0 to 255 is the selected range; it could defined as any data range. However, hue must vary from 0 360 to define the entire sphere (Buchanan 1979).

The variance of intensity and hue in RGB

saturation

hue

APPL 97

Whe

R

r,

M

m

At lewith corre

The e

The e

If M

If I ≤

If I >

The e

IfIfIfIf

1.0.

YING SPECTRAL ENHANCEMENT

0.5,

0.5,

quations for calculating hue in the range of 0 to 360 are:

M = m, H = 0 R = M, H = 60 (2 + b - g) G = M, H = 60 (4 + r - b) B = M, H = 60 (6 + g - r)

SM m+---------------=

S M m–2 M– m–------------------------=

re:

, G, B are each in the range of 0 to 1.0.

g, b are each in the range of 0 to 1.0.

= largest value, r, g, or b

= least value, r, g, or b

ast one of the R, G, or B values is 0, corresponding to the color the largest value, and at least one of the R, G, or B values is 1, sponding to the color with the least value.

quation for calculating intensity in the range of 0 to 1.0 is:

quations for calculating saturation in the range of 0 to 1.0 are:

= m, S = 0

I M m+2

---------------=

M m–

Where:

R, G, B are each in the range of 0 to

M = largest value, R, G, or B

m = least value, R, G, or B

YSIS FOR ARCGIS

RGB to IHS

1

3

USING IMAGE ANAL98

1. Click the Image Analysis dropdown arrow, point to Spectral Enhancement, and click RGB to IHS.

2. Click the Input Image dropdown arrow, and click the image you want to use, or navigate to the directory where it is stored.


4. Click OK.

4

2

RGB to IHS

Using RGB to IHS applies an algorithm that transforms red, green, and blue (RGB) values to the intensity, hue, and saturation (IHS) values.

99

IHS to RGB

to 1.0 are:

.0:

---

0----

0---

APPLYI

IHS to IHS traappliedmore fucirculadynamthat I omodel,they mfull IHSthe parperceivinput im

It is nobe derior S as RGB sp

In anothigh-frintensitChavezmerge 1991).

The algRGB fu

Given:

If I 0.5

If I > 0

The eq

m =

NG SPECTRAL ENHANCEMENT

Landsat TM with SPOT panchromatic imagery (Chavez

orithm used by Image Analysis for ArcGIS for the IHS to nction is (Conrac 1980):

H in the range of 0 to 360; I and S in the range of 0 to 1.0

,

.5,

uations for calculating R in the range of 0 to 1.0 are:

M I 1 S+( )=

M I S I S( )–+=

2 1 M–⋅

If H < 60,

If 60 H < 120,

If 120 H < 240,

If 240 H < 300,

If 300 H 360,

B M=

B m M m–( ) 120 H–60

-------------------+=

B M=

B m M m–( ) H 24–60

----------------+=

B M=

RGB is intended as a complement to the standard RGB to nsform. In the IHS to RGB algorithm, a min-max stretch is to either intensity (I), saturation (S), or both, so that they lly utilize the 0 to 1 value range. The values for hue (H), a

r dimension, are 0 to 360. However, depending on the ic range of the DN values of the input image, it is possible r S or both occupy only a part of the 0 to 1 range. In this a min-max stretch is applied to either I, S, or both, so that ore fully utilize the 0 to 1 value range. After stretching, the image is retransformed back to the original RGB space. As

ameter Hue is not modified, it largely defines what we e as color, and the resultant image looks very much like the age.

t essential that the input parameters (IHS) to this transform ved from an RGB to IHS transform. You could define I and/other parameters, set Hue at 0 to 360, and then transform to ace. This is a method of color coding other data sets.

her approach (Daily 1983), H and I are replaced by low- and equency radar imagery. You can also replace I with radar y before the IHS to RGB transform (Holcomb 1993). evaluates the use of the IHS to RGB transform to resolution

If H < 60,

If 60 H < 180,

If 180 H < 240,

If 240 H 360,

The equations for calculating G in the range of 0

If H < 120,

If 120 H < 180,

If 180 H < 300,

If 300 H 360,

Equations for calculating B in the range of 0 to 1

R m M m–( ) H60------ +=

R M=

R m M m–( ) 240 H–60

----------------+=

R m=

G m=

G m M m–( ) H 12–60

---------------+=

G M=

G m M m–( ) 360 H–60

-------------------+=

YSIS FOR ARCGIS

Converting IHS to RGB

1

3

USING IMAGE ANAL100

1. Click the Image Analysis dropdown arrow, point to Spectral Enhancement, and click IHS to RGB.

2. Click the Input Image dropdown arrow, and click the image you want to use, or navigate to the directory where it is stored.


4. Click OK.

4

2

IHS to RGB

Using IHS to RGB applies an algorithm that transforms intensity, hue, and saturation (IHS) values to red, green, and blue (RGB) values.

101

Vegetat ive Indices effects in satellite white images of three ratios, may

ely used by ery for mineral 3/1.

been

DVI) =

; Tucker 1979

IR R–IR R+----------------

0.5

APPLYI

MappinimagerAnalys

Indicescombinsimplis

or more

In man

These rspectramolecugives in

Appl

• Indvevajuddifori

NG SPECTRAL ENHANCEMENT

lar bonds in the (surface) material. Thus, the ratio often formation on the chemical composition of the target.

ications

ices are used extensively in mineral exploration and getation analysis to bring out small differences between rious rock types and vegetation classes. In many cases, iciously chosen indices can highlight and enhance ferences that cannot be observed in the display of the ginal color bands.

Source: Modified from Sabins 1987; Jensen 1996

g vegetation is a common application of remotely sensed y. To help you find vegetation quickly and easily, Image is for ArcGIS includes a Vegetative Indices feature.

are used to create output images by mathematically ing the DN values of different bands. These may be tic:

(Band X - Band Y)

complex:

y instances, these indices are ratios of band DN values:

atio images are derived from the absorption/reflection of the material of interest. The absorption is based on the

BandX BandY–BandX BandY+-----------------------------------------

BandXBandY-----------------

• Indices can also be used to minimize shadowand aircraft multispectral images. Black andindividual indices, or a color combination ofbe generated.

• Certain combinations of TM ratios are routingeologists for interpretation of Landsat imagtype. For example: Red 5/7, Green 5/4, Blue

Index examples

The following are examples of indices that have preprogrammed in Image Analysis for ArcGIS:

• IR/R (infrared/red)• SQRT (IR/R)• Vegetation Index = IR-R

• Normalized Difference Vegetation Index (N

• Transformed NDVI (TNDVI) = IR R–IR R+---------------- +

YSIS FOR ARCGIS102

The fsome

Ima

Imagcombcomb

(

D

yieldvege

Bandabsobaselabso

USING IMAGE ANAL

tation.

ratios are also commonly used. These are derived from the rption spectra of the material of interest. The numerator is a ine of background absorption and the denominator is an rption peak.

ollowing table shows the infrared (IR) and red (R) band for common sensors (Tucker 1979, Jensen 1996):

ge algebra

e algebra is a general term used to describe operations that ine the pixels of two or more raster layers in mathematical inations. For example, the calculation:

infrared band) - (red band)

Nir - DNred

s a simple, yet very useful, measure of the presence of

Sensor IR Band R Band

Landsat MSS 4 2

SPOT XS 3 2

Landsat TM 4 3

NOAA AVHRR 2 1

103

Using Vegetative Indices

1

2

6

APPLYING SPECTRAL ENHANCEMENT

1. Click the Image Analysis dropdown arrow, point to Spectral Enhancement, and click Vegetative Indices.

2. Navigate to the directory where the image is stored.3. Click the dropdown list to add the Near Infrared Band

number.4. Click the dropdown list to add the Visible Red Band

number.5. Choose the Desired Index from the dropdown list.6. Navigate to the directory where the Output Image should


4

3

5

7

YSIS FOR ARCGIS

Color IR to Natural Color

rs in natural colors.

104

This fudata socolors fdesignaof imagschemeEnhancone ban

When ato apprreal wodark inbe assigto coloto chan

USING IMAGE ANAL

The infrared image of a golf course.

nction lets you simulate natural colors from other types of that the output image is a fair approximation of the natural rom an infrared image. If you are not familiar with the bands ted to reflect infrared and natural color for a particular type ery, Image Analysis for ArcGIS can help you apply either through the Color IR to Natural Color choice in Spectral ement. You cannot apply this feature to images having only d of data (i.e. grayscale images).

n image is displayed in natural color, the bands are arranged oximate the most natural representation of the image in the rld. Vegetation becomes green in color, and water becomes color. To create natural color, certain bands of data need to ned to red, green, and blue. You will need to assign bands

r depending on how many bands are in the image you want ge to natural color.

After using Color IR to Natural Color, the image appea

105

Using Color IR to Natural Color

2

1

6

APPLYING SPECTRAL ENHANCEMENT

1. Click the Image Analysis dropdown arrow, point to Spectral Enhancement, and click Color IR to Natural Color.

2. Click the dropdown arrow or navigate to the directory to select the Input Image.

3. Click the Near Infrared Band dropdown arrow, and select the appropriate band.

4. Click the Visible Red Band dropdown arrow, and select the appropriate band.

5. Click the Visible Green Band dropdown arrow, and select the appropriate band.


7. Click OK.

5

4

3

7

YSIS FOR ARCGIS
USING IMAGE ANAL106

107

1Per

pulate, and ation. A GIS e GIS database r any other data ed to describe ining program, iller 1990). GIS e, from ortation routing.

l information—

litan area?n the earth’s

gered species?age Analysis for a GIS. You can

ls contained in mation.

IN THIS C

• PerformAnalysis

• Perform

• Using R

• Using S

8
forming GIS Analysis
A GIS is a unique system designed to input, store, retrieve, manianalyze layers of geographic data to produce interpretable informshould also be able to create reports and maps (Marble 1990). Thmay include computer images, hardcopy maps, statistical data, othat is needed in a study. Although the term GIS is commonly ussoftware packages, a true GIS includes knowledgeable staff, a trabudgets, marketing, hardware, data, and software (Walker and Mtechnology can be used in almost any geography-related disciplinLandscape Architecture to natural resource management to transp

The central purpose of a GIS is to turn geographic data into usefuthe answers to real-life questions—questions such as:

• How should political districts be redrawn in a growing metropo• How can we monitor the influence of global climatic changes o

resources?• What areas should be protected to ensure the survival of endanThis chapter is about using the different analysis functions in ImArcGIS to better use the images, data, maps, and so on located inuse GIS technology in any geography related discipline. The tooGIS Analysis will help you turn geographic data into useful infor

HAPTER

ing Neighborhood

ing Thematic Change

ecode

ummarize Areas

YSIS FOR ARCGIS

Informat ion versus data

108

Informrelevan

• “Tfile

• “Lde

You cainformmust befor baldcover d

For thisassessmdefinedAlthoudatabasThe dathe org

A majoanalysito creatthem.

Once thlayers cinformvisuallyinformusing th

USING IMAGE ANAL

s phase, data layers are combined and manipulated in order e new layers and to extract meaningful information from

e database (layers and attribute data) is assembled, the an be analyzed and new information extracted. Some

ation can be extracted simply by looking at the layers and comparing them to other layers. However, new

ation can be retrieved by combining and comparing layers e following procedures.

ation, as opposed to data, is independently meaningful. It is t to a particular problem or question:

he land cover at coordinate N875250, E757261 has a data value 8,” is data.

and cover with a value of 8 are on slopes too steep for velopment,” is information.

n input data into a GIS and output information. The ation you wish to derive determines the type of data that input. For example, if you are looking for a suitable refuge eagles, zip code data is probably not needed, while land ata may be useful.

reason, the first step in any GIS project is usually an ent of the scope and goals of the study. Once the project is , you can begin the process of building the database. gh software and data are commercially available, a custom e must be created for the particular project and study area.

tabase must be designed to meet the needs and objectives of anization.

r step in successful GIS implementation is analysis. In the

109

Neighborhood Analysiss value within the ses with the low

class values that ed to identify the

highlight

an window whose

class values are pixels based on

PERFOR

Neighbthat takconvolconvolof analsum, an

With a raster lfilterinbe confanalysidata (S

Every psurrounis deterThese o

Neighbseveralof pixe

• Devahois

• Diwihe

• Maof low

• Mawicla

MING GIS ANALYSIS

nsity—outputs the number of pixels that have the same class lue as the center (analyzed) pixel. This is also a measure of mogeneity (sameness), based upon the analyzed pixel. This often useful in assessing vegetation crown closure.versity—outputs the number of class values that are present thin the window. Diversity is also a measure of terogeneity (difference). jority—outputs the class value that represents the majority

the class values in the window. This option operates like a -frequency filter to clean up a salt and pepper layer. ximum—outputs the greatest class value within the

ndow. This can be used to emphasize classes with the higher ss values or to eliminate linear features or boundaries.

orhood Analysis applies to any image processing technique es surrounding pixels into consideration, such as

ution filtering and scanning. This is similar to the ution filtering performed on continuous data. Several types yses can be performed, such as boundary, density, mean, d so on.

process similar to the convolution filtering of continuous ayers, thematic raster layers can also be filtered. The GIS g process is sometimes referred to as scanning, but is not to used with data capture via a digital camera. Neighborhood s is based on local or neighborhood characteristics of the tar and Estes 1990).

ixel is analyzed spatially, according to the pixels that d it. The number and the location of the surrounding pixels mined by a scanning window, which is defined by you. perations are known as focal operations.

orhood analysis creates a new thematic layer. There are types of analysis that can be performed upon each window ls, as described below:

• Minimum—outputs the least or smallest claswindow. This can be used to emphasize clasclass values.

• Minority—outputs the least common of the are within the window. This option can be usleast common classes. It can also be used todisconnected linear features.

• Rank—outputs the number of pixels in the scvalue is less than the center pixel.

• Sum—totals the class values. In a file whereranked, totaling enables you to further rank their proximity to high-ranking pixels.

YSIS FOR ARCGIS

Performing Neighborhood Analysis

1

2

3

4

6

USING IMAGE ANAL110

1. Click the Image Analysis dropdown arrow, point to GIS Analysis, and click Neighborhood.


3. Click the Neighborhood Function dropdown arrow, and choose the function you want to use.

4. Click the Neighborhood Shape dropdown arrow, and choose the shape you want to use.

5. Click the Matrix size dropdown arrow, and choose the size you want to use.


7. Click OK.

5

7

Neighborhood Analysis

Neighborhood Analysis applies to any analysis function that takes neighboring pixels into account. This function creates a new thematic layer.

The Neighborhood Analysis process is similar to convolution filtering. Every pixel is spatially analyzed according to the pixels surrounding it.

The different types of analysis that can be performed on each window of pixels are listed in the dropdown menu for Neighborhood Function.

111

Thematic Change categorizations

ount and the type binations of

ed into a matrix. y in any way. The

PERFOR

Themaof yourof chanchange

ThemaThe firnumbe

MING GIS ANALYSIS

tic Change identifies areas that undergo change over time. Typically, you use Thematic Change after you perform data. By using the categorizations of Before Theme and After Theme in the dialog, you can quantify both the amges that take place over time. Image Analysis for ArcGIS produces a thematic image that has all the possible com.

tic Change creates an output image from two input raster files. The class values of the two input files are organizst input file specifies the columns of the matrix, and the second one specifies the rows. Zero is not treated speciallr of classes in the output file is the product of the number of classes from the two input files.

Both before and after images prior to performing Thematic Change.

YSIS FOR ARCGIS

Performing Thematic Change

1

2

4

USING IMAGE ANAL112

1. Click the Image Analysis dropdown arrow, point to GIS Analysis, and click Thematic Change.

2. Click the Before Theme dropdown arrow, and click the file you want to use, or navigate to the directory where it is stored.

3. Click the After Theme dropdown arrow, and click the file you want to use, or navigate to the directory where it is stored.


5. Click OK.

5

3

Thematic Change

Use Thematic Change to identify areas that have undergone change over time.

113

nts you see the

PERFOR

The folcombin

MING GIS ANALYSIS

lowing illustration is an example of the previous image after undergoing Thematic Change. In the Table of conteation of classes from the Before and After images.

Note the areas of classification that show the changes between 1973 and 1994.

YSIS FOR ARCGIS

Recode

code by class name.

d and grouped class

114

By usinRecodiclasses

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USING IMAGE ANAL

the actual recoding process, which rewrites the Attribute ing the information from your grouping process.

ee recoding methods described below are more accurately ed as three methods of grouping the classified image to get for the recode process. These methods are recoding by class ecoding by symbology, and recoding a previously grouped The following exercises will take you through each of the coding methods.

South Carolina soils after the recode. Notice the changenames in the Table of contents.

g Recode, class values can be recoded to new values. ng involves the assignment of new values to one or more of an existing file. Recoding is used to:

uce the number of classesmbine classesign different class values to existing classesite class name and color changes to the Attribute table

n ordinal, ratio, or interval class numbering system is used, g can be used to assign classes to appropriate values. ng is often performed to make later steps easier. For e, in creating a model that outputs good, better, and best t may be beneficial to recode the input layers so all of the sses have the highest class values.

n also use Recode to save any changes made to the color or class names of a classified image to the Attribute Table r use. Just saving an image will not record these changes.

ng an image involves two major steps. First, you must group rete classes together into common groups. Secondly, you

Thematic Image of South Carolina soil types before Re

115

Performing Recode by class name

2

3

4

PERFORMING GIS ANALYSIS

You will group the classified image in the ArcMap Table of contents, and then perform the recode.

1. Click Add Data to open a classified image.2. Identify the classes you want to group together in the

Table of contents.3. Triple-click each class you wish to rename, and rename

it.4. Click the color of each class, and change it to the color

scheme you want to use.

YSIS FOR ARCGIS

5. Click the Image Analysis dropdown arrow, point to GIS

5

6

USING IMAGE ANAL116

Analysis, and click Recode.6. Navigate to the directory where the Output Image should


7

117

Performing Recode by symbology4

7


This process will show you how to recode by symbology. You will see similarities with recoding by class name, but you should be aware of some different procedures. You will notice that steps 1-3 and 10-12 are the same as the previous Recode exercise.

1. Click Add Data to open an classified image.2. Identify the classes you want to group together.3. Click the colors of the classes to change to your desired

color scheme.4. Double-click the image name in the Table of contents.5. Click the Symbology tab in the Layer Properties dialog.6. Press the Ctrl key while clicking on the first set of classes

you want to group together.7. Right click on the selected classes, and click Group

Values.8. Click in the Label column and type the new name for the

class.9. Follow steps 5-7 to group the rest of your classes.10. Click Apply and OK.11. Click the Image Analysis dropdown arrow, point to GIS

Analysis, and click Recode.12. Navigate to the directory where the Output Image should


6

5

8

YSIS FOR ARCGIS

Recoding with previously grouped image

4

2

3

USING IMAGE ANAL118

You may need to open an image that has been classified and grouped in another program such as ERDAS

IMAGINE®. These images may have more than one valid attribute column that can be used to perform the recode.

1. Click Add Data and add the grouped image.2. Click the Image Analysis dropdown arrow, point to GIS

Analysis, and click Recode.3. Click the Map Pixel Value through Field dropdown

arrow, and select the attribute you want to use to recode the image.


5. Click OK.

5

PERF 119

The fin ER

ORMING GIS ANALYSIS

After Recode in Image Analysis for ArcGIS

ollowing images depict soil data that was previously grouped DAS IMAGINE.

Previously grouped before Recode

YSIS FOR ARCGIS

Summarize Areas

120

Image methodthe Thelimit th

SummaAnalysin tabustatisticincludihectare

Summain prepjurisdicarea to geograsummaparticu

USING IMAGE ANAL

Analysis for ArcGIS also provides Summarize Areas as a of assessing change in thematic data. Once you complete matic Change analysis, you can use Summarize Areas to e analysis to include only a portion of the entire image.

rize Areas works by using a feature theme or an Image is for ArcGIS theme to compile information about that area lar format. Summarize Areas produces cross-tabulation s that compare class value areas between two thematic files,

ng number of points in common, number of acres (or s or square miles) in common, and percentages.

rize Areas might be used to assist a regional planning office aring a study of urban change for certain counties within the tion or even within one county or city. A file containing the be inventoried can be summarized by a file for the same phical area containing the land cover categories. The ry report could indicate the amount of urban change in a lar area of a larger thematic change.

121

Using Summarize Areas

1

3

4


1. Click the Image Analysis dropdown arrow, point to GIS Analysis, and click Summarize Areas.

2. Click the Zone theme dropdown arrow, and click on the theme you want to use, or navigate to the directory where it is stored.

3. Click on the dropdown arrow for the Zone Attribute, and click on the condition for each value of the attribute.

4. Click on the dropdown arrow for the Class Theme, and click on the class theme, or navigate to the directory where it is stored.

5. Click OK.

2

5

Summarize Areas

Use Summarize Areas to produce cross-tabulation statistics for comparison of class value areas between two thematic files, or one thematic and one shapefile, including number of points in common, number of acres (or hectares or square miles) in common, and percentages.

YSIS FOR ARCGIS
USING IMAGE ANAL122

123

1Usi

to interpret and cGIS provides a ocedures offered differences, set n about Subset hapter 4 “Using at menu.

ow to use:

IN THIS C

• Image D

• Layer S

9
ng Utilities
The core of Image Analysis for ArcGIS is the ability it gives youmanipulate your data. The Utilities part of Image Analysis for Arnumber of features for you to use in this capacity. The different prin the Utilities menu allow you to alter your images in order to seenew parameters, create images, or subset images. The informatioImage, Create New Image, and Reproject Image can be found in cData Preparation” since the options are also accessible through th

This chapter will explain the following functions and show you h

• Image Difference• Layer Stack

HAPTER

ifference

tack

YSIS FOR ARCGIS

Image Di f ference (darker) change n color. The ) change greater as of positive and s of no change are e colors to select

age.

lue, then assign

value then assign

124

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USING IMAGE ANAL

ave increased in reflectance. This may mean clearing of d areas. Dark areas have decreased in reflectance. This may n area has become more vegetated, or the area was dry and wet.

ond image is the Highlight Difference image. This thematic ivides the changes into five categories. The five categories reased, Some Decrease, Unchanged, Some Increase, and ed.

age Difference function gives you the ability to iently perform change detection on aspects of an area by ing two images of the same place from different times.

age Difference tool is particularly useful in plotting mental changes such as urban sprawl and deforestation or

truction caused by a wildfire or tree disease. It is also a ool to use in determining crop rotation or the best new place lop a neighborhood.

Difference is used for change analysis with imagery that the same area at different points in time. With Image nce, you can highlight specific areas of change in whatever t you choose. Two images are generated from this image-to-omparison; one is a grayscale continuous image, and the a five-class thematic image.

st image generated from Image Difference is the Difference The Difference image is a grayscale image composed of and continuous data. This image is created by subtracting ore Image from the After Image. Since Image Difference tes change in brightness values over time, the Difference imply reflects that change using a grayscale image. Brighter

The Decreased class represents areas of negativegreater than the threshold for change and is red iIncreased class shows areas of positive (brighterthan the threshold and is green in color. Other arenegative change less than the thresholds and areatransparent. For your application, you may edit thany color desired for your study.

Algorithm

Subtract two images on a pixel by pixel basis.

1. Subtract the Before Image from the After Im2. Convert the decrease percentage to a value.3. Convert the increase percentage to a value.4. If the difference is less than the decrease va

the pixel to Class 1 (Decreased).5. If the difference is greater than the increase

the pixel to Class 5 (Increased).

125

Using Image Difference

1

4

6

8

USING UTILITIES

1. Click the Image Analysis dropdown arrow, point to Utilities, and click Image Difference.

2. Click the Before Theme dropdown arrow, and click the file you want to use, or navigate to the directory where it is stored.

3. Click the After Theme dropdown arrow and click the file you want to use, or navigate to the directory where it is stored.

4. Choose As Percent or As Value for the Highlight Changes.

5. Enter the Increases and Decreases values.6. Click the color bar to choose the color you want to

represent the increases and decreases.7. Type the Image Difference file name, or navigate to the

directory where it should be stored.8. Type the Highlight Change file name, or navigate to the

directory where it should be stored.9. Click OK.

The Image Difference Output file showing highlight change.

3

5

7

9

2

YSIS FOR ARCGIS

Layer Stack

LBAND image and TM.

126

Layer Sto formimagerexamplfinish wstackinimages

Stackinyou inithe ordbands w

There avisualizviewinyou havindividanalyzeshow d

An exaseparatconsoli

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USING IMAGE ANAL

dates the bands of data into one file.

age on this page is an example of a Layer Stack output. The ed are from the Amazon, and the red and blue bands were from one image, while the green band was chosen from the

tack lets you stack layers from different images in any order a single theme. It is useful for combining different types of y for analysis such as multispectral and radar data. For e, if you stack three single-band grayscale images, you ith one three band image. In general, you will find that

g images is most useful for combining grayscale single-band into multiband images.

g works based on the order in the Table of contents. Before tiate stacking, you should first ensure that the images are in er that you want. This order represents the order in which the

ill be arranged in the output file.

re several applications of this feature such as change ation, combining and viewing multiple resolution data, and

g disparate data types. Layer Stack is particularly useful if e received a multispectral dataset with each of the

ual bands in separate files. You can also use Layer Stack to datasets taken during different seasons when different sets ifferent stages for vegetation in an area.

mple of a multispectral dataset with individual bands in e files would be Landsat TM data. Layer stack quickly

A stacked image with bands 1 and 3 taken from Amazonthe rest of the layers take from Amazon

127

Using Layer Stack

1

USING UTILITIES

1. Click the Image Analysis dropdown arrow, point to Utilities, and click Layer Stack.

2. Select a currently open layer, and click Add to include it in the layer stack.

3. Click the browse button to navigate to a file containing layers you want to add to the layer stack.

4. Select any files you want to remove from the layer stack and click Remove.


6. Click OK.

2

3

5

4

6

YSIS FOR ARCGIS
USING IMAGE ANAL128

129

1Und

finite number of alues. If a pixel that corresponds

he original data, may simply f a classified re, urban, and so

categories:

pervised d Classification.

IN THIS C

• The Clas

• Classific

• Unsupe

• Supervi

• Classific

0
erstanding Classification
Multispectral classification is the process of sorting pixels into a individual classes, or categories of data, based on their data file vsatisfies a certain set of criteria, the pixel is assigned to the class to that criteria.

Depending on the type of information you want to extract from tclasses may be associated with known features on the ground or represent areas that look different to the computer. An example oimage is a land cover map that shows vegetation, bare land, pastuon.

This chapter covers the two ways to classify pixels into different

• Unsupervised Classification• Supervised ClassificationThe differences in the two are basically as their titles suggest. SuClassification is more closely controlled by you than Unsupervise

HAPTER

sification Process

ation Tips

rvised Classification

sed Classification

ation Decision Rules

1

YSIS FOR ARCGIS

The Classi f icat ion Process

ed. It enables you s to uncover

hese patterns do characteristics of areas of a clusters of pixels s, it may be more r spectral izable categories.

a itself for the when less is hen the analyst’s ing to the

assification is erpreted.

efines a training class, and is used the pixels in the ametric class

etric class per band minima

eters (e.g., mean e training sample can generate res can be used to m likelihood) to

130

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USING IMAGE ANAL

rvised training

ised training is closely controlled by the analyst. In this , you select pixels that represent patterns or land cover s that you recognize, or that you can identify with help from urces, such as aerial photos, ground truth data, or maps.

edge of the data, and of the classes desired, is required before cation.

tifying patterns, you can instruct the computer system to pixels with similar characteristics. If the classification is e, the resulting classes represent the categories within the t you originally identified.

with a decision rule (explained below) to assign image file to a class. Signatures contain both pardefinitions (mean and covariance) and non-paramdefinitions (parallelepiped boundaries that are theand maxima).

A parametric signature is based on statistical paramand covariance matrix) of the pixels that are in thor cluster. Supervised and unsupervised training parametric signatures. A set of parametric signatutrain a statistically-based classifier (e.g., maximudefine the classes.

rn recognit ion

recognition is the science—and art—of finding meaningful s in data, which can be extracted through classification. By y and spectrally enhancing an image, pattern recognition performed with the human eye; the human brain tically sorts certain textures and colors into categories.

mputer system, spectral pattern recognition can be more ic. Statistics are derived from the spectral characteristics of ls in an image. However, in Supervised Classification, the s are derived from the training samples, and not the entire After the statistics are derived, pixels are sorted based on atical criteria. The classification process breaks down into ts: training and classifying (using a decision rule).

ing

e computer system must be trained to recognize patterns in . Training is the process of defining the criteria by which

atterns are recognized (Hord 1982). Training can be ed with either a supervised or an unsupervised method, as

ed below.

Unsupervised training

Unsupervised training is more computer-automatto specify some parameters that the computer usestatistical patterns that are inherent in the data. Tnot necessarily correspond to directly meaningfulthe scene, such as contiguous, easily recognized particular soil type or land use. They are simply with similar spectral characteristics. In some caseimportant to identify groups of pixels with similacharacteristics than it is to sort pixels into recogn

Unsupervised training is dependent upon the datdefinition of classes. This method is usually usedknown about the data before classification. It is tresponsibility, after classification, to attach meanresulting classes (Jensen 1996). Unsupervised cluseful only if the classes can be appropriately int

Signatures

The result of training is a set of signatures that dsample or cluster. Each signature corresponds to a

UNDE 131

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RSTANDING CLASSIFICATION

n a nonparametric rule is set, the pixel is tested against all of ignatures with nonparametric definitions. This rule results in llowing conditions:

f the nonparametric test results in one unique class, the pixel s assigned to that class.f the nonparametric test results in zero classes (for example, he pixel lies outside all the nonparametric decision oundaries), then the pixel is assigned to a class called nclassified.

lelepiped is the only nonparametric decision rule in Image ysis for ArcGIS.

ision rule

the signatures are defined, the pixels of the image are sorted classes based on the signatures by use of a classification ion rule. The decision rule is a mathematical algorithm that, data contained in the signature, performs the actual sorting of s into distinct class values.

ametric decision rule

rametric decision rule is trained by the parametric signatures. e signatures are defined by the mean vector and covariance ix for the data file values of the pixels in the signatures. When ametric decision rule is used, every pixel is assigned to a class the parametric decision space is continuous (Kloer 1994). e are three parametric decision rules offered:

inimum distanceahalanobis distanceaximum likelihood

parametric decision rule

YSIS FOR ARCGIS

Classi f icat ion t ips is begun by on, using as a general

d

of desired classes es from the data. els that represent

hen you want to lected training or when you can sent each class. In tly classify small se Supervised

etermined by so that you can tter suited to bles you to define ot in contiguous,

ou do not classify ols”), they will be pervised taking a training tion.

132

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USING IMAGE ANAL

85. Florida Land Use, Cover and Forms Classification stem. Florida Department of Transportation, Procedure No. 0-010-001-a. chigan Land Use Classification and Reference Committee. 75. Michigan Land Cover/Use Classification System. nsing, Michigan: State of Michigan Office of Land Use.

tates or government agencies may also have specialized land er studies.

define the classes later, then the application is beunsupervised training. Unsupervised training enamany classes easily, and identify classes that are neasily recognized regions.

If you have areas that have a value of zero, and ythem as NoData (see chapter 3 “Applying data toassigned to the first class when performing UnsuClassification. You can assign a specific class bysample when performing a Supervised Classifica

sif ication scheme

, classification is performed with a set of target classes in uch a set is called a classification scheme (or classification

). The purpose of such a scheme is to provide a framework anizing and categorizing the information that can be ed from the data (Jensen 1983). The proper classification includes classes that are both important to the study and ible from the data on hand. Most schemes have a hical structure, which can describe a study area in several f detail.

ber of classification schemes have been developed by ists who have inventoried a geographic region. Some ces for professionally-developed schemes are listed below:

derson, J. R., et al. 1976. “A Land Use and Land Cover assification System for Use with Remote Sensor Data.” U.S. ological Survey Professional Paper 964. wardin, Lewis M., et al. 1979. Classification of Wetlands d Deepwater Habitats of the United States. Washington, C.: U.S. Fish and Wildlife Service.rida Topographic Bureau, Thematic Mapping Section.

It is recommended that the classification processdefining a classification scheme for the applicatipreviously developed schemes, like those above,framework.

Supervised versus UnsuperviseClassif ication

In supervised training, it is important to have a setin mind, and then create the appropriate signaturYou must also have some way of recognizing pixthe classes that you want to extract.

Supervised classification is usually appropriate widentify relatively few classes, when you have sesites that can be verified with ground truth data, identify distinct, homogeneous regions that repreImage Analysis for ArcGIS, if you need to correcareas with actual representation, you should chooClassification.

On the other hand, if you want the classes to be dspectral distinctions that are inherent in the data

UNDE 133

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For mmergcompvery underecomclass

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ssifying enhanced data

any specialized applications, classifying data that have been ed, spectrally merged or enhanced—with principal onents, image algebra, or other transformations—can produce specific and meaningful results. However, without rstanding the data and the enhancements used, it is

mended that only the original, remotely-sensed data be ified.

i t ing dimensions

ugh Image Analysis for ArcGIS allows an unlimited number ers of data to be used for one classification, it is usually wise uce the dimensionality of the data as much as possible. Often,

in layers of data are redundant or extraneous to the task at . Unnecessary data take up valuable disk space, and causes the uter system to perform more arduous calculations, which

s down processing.

YSIS FOR ARCGIS

Unsupervised Classi f icat ion/Categor ize Imageistance to assign a s with a specified f existing hat those means

not biased to the algorithms.

, the means of N iteration, a new actual spectral e initial arbitrary efining clusters in re is little change

e space along a rdinates (µ1-σ1,

1, µ2+σ2, µ3+σ3, trated below. The en (µA-σA, µB-

134

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If you nshould used byareas suwrongl

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USING IMAGE ANAL

es the pixels, redefines the criteria for each class, and es again, so that the spectral distance patterns in the data lly emerge.

ATA clustering

TA is iterative in that it repeatedly performs an entire cation (outputting a thematic raster layer) and recalculates s. Self-Organizing refers to the way in which it locates with minimum user input.

initial cluster means are evenly distributed betweσB) and (µA+σA, µB+σB).

rvised training requires only minimal initial input from you. er, you have the task of interpreting the classes that are by the unsupervised training algorithm. Unsupervised is also called clustering, because it is based on the natural gs of pixels in image data when they are plotted in feature

eed to classify small areas with small representation, you use Supervised Classification. Due to the skip factor of 8 the Unsupervised Classification signature collection, small ch as wetlands, small urban areas, or grasses can be

y classified on rural data sets.

ters

s are defined with a clustering algorithm, which often uses any of the pixels in the input data file for its analysis. The ng algorithm has no regard for the contiguity of the pixels ine each cluster.

rative Self-Organizing Data Analysis Technique TA) (Tou and Gonzalez 1974) clustering method uses

l distance as in the sequential method, but iteratively

The ISODATA method uses minimum spectral dcluster for each candidate pixel. The process beginnumber of arbitrary cluster means or the means osignatures, and then it processes repetitively, so tshift to the means of the clusters in the data.

Because the ISODATA method is iterative, it is top of the data file, as are the one-pass clustering

Ini t ial cluster means

On the first iteration of the ISODATA algorithmclusters can be arbitrarily determined. After eachmean for each cluster is calculated, based on the locations of the pixels in the cluster, instead of thcalculation. Then, these new means are used for dthe next iteration. The process continues until thebetween iterations (Swain 1973).

The initial cluster means are distributed in featurvector that runs between the point at spectral cooµ2-σ2, µ3-σ3, ... µn-σn) and the coordinates (µ1+σ... µn+σn). Such a vector in two dimensions is illus

UNDE 135

Pix

Pixelimag

The smeanis thewith of eathe p

Consmeanresul

are recalculated, process is e new cluster

ter


is calculated. The pixel is assigned to the cluster whose mean closest. The ISODATA function creates an output image file a thematic raster layer as a result of the clustering. At the end ch iteration, an image file exists that shows the assignments of ixels to the clusters.

idering the regular, arbitrary assignment of the initial cluster s, the first iteration of the ISODATA algorithm always gives ts similar to those in this illustration.

el analysis

s are analyzed beginning with the upper left corner of the e and going left to right, block by block.

pectral distance between the candidate pixel and each cluster

ISODATA Arbitrary Clusters5 arbitrary cluster means in two-dimensional spectral space

µΒ+σΒ

µΒ

µΒ−σΒ

µΑ+σΑ µΑ−σΑµΑ

Band Adata file values

Ban

d B

data

file

val

ues

For the second iteration, the means of all clusterscausing them to shift in feature space. The entirerepeated—each candidate pixel is compared to thmeans and assigned to the closest cluster mean.

1

Cluster2

Cluster3

Cluster4

Clus5


Ban

d B

data

file

val

ues

Cluster

YSIS FOR ARCGIS136

Per

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USING IMAGE ANAL

ossible for the percentage of unchanged pixels to never erge or reach T (the convergence threshold). Since you are not to control the convergence threshold, it may be beneficial to tor the percentage, or specify a reasonable maximum number rations, M, so that the program does not run indefinitely.

centage unchanged

each iteration, the normalized percentage of pixels whose nments are unchanged since the last iteration is displayed on ialog. When this number reaches T (the convergence hold), the program terminates.


Ban

d B

data

file

val

ues

1

Cluster2

Cluster3

Cluster4

Cluster5

Cluster

137

Performing Unsupervised Classif ication/

1

2

UNDERSTANDING CLASSIFICATION

Categorize Image

1. Click the Image Analysis dropdown arrow, point to Classification, and click Unsupervised/Categorize.

2. Click the Input Image dropdown arrow, or navigate to the directory where it is stored.

3. Type or click the arrows to enter the Desired Number of Classes.


5. Click OK.

4

3

YSIS FOR ARCGIS

Supervised Classi f icat ion

138

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USING IMAGE ANAL

s possible (Star and Estes 1990). However, some ground y not be very accurate due to a number of errors and acies.

ised classification requires a priori (already known) ation about the data, such as:

hat type of classes need to be extracted? Soil type? Land ? Vegetation?

hat classes are most likely to be present in the data? That is, ich types of land cover, soil, or vegetation (or whatever) are resented by the data?

rvised training, you rely on your own pattern recognition nd a priori knowledge of the data to help the system ine the statistical criteria (signatures) for data classification.

ct reliable samples, you should know some information—patial or spectral—about the pixels that you want to classify.

ation of a specific characteristic, such as a land cover type, known through ground truthing. Ground truthing refers to uisition of knowledge about the study area from field work, s of aerial photography, personal experience, and so on. truth data are considered to be the most accurate (true) data le about the area of study. It should be collected at the same the remotely sensed data, so that the data correspond as

139

Performing Supervised Classif ication

1

3

2

6

7

UNDERSTANDING CLASSIFICATION

1. Click the Image Analysis dropdown arrow, point to Classification, and click Supervised.


3. Click the Signature Features dropdown arrow, and click the file you want to use, or navigate to the directory where it is stored.

4. Click the Class Name Field dropdown arrow, and click the field you want to use.

5. Choose All Features or Selected Features to use during classification.

6. Click the Classification Rule dropdown arrow, and click the rule you want to use.


8. Click OK.

8

4

5

YSIS FOR ARCGIS

Classi f icat ion decis ion rules

spectral distance) rement vector for

signature.

by the lines from atures. The losest mean.

is based on the

yi)2

140

Once anext steanalyzecomparalgorithdecisioAnalyswith sta

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USING IMAGE ANAL

The equation for classifying by spectral distanceequation for Euclidean distance:

SDxyc µci Xx–(

i 1=

n

∑=

set of reliable signatures has been created and evaluated, the p is to perform a classification of the data. Each pixel is d independently. The measurement vector for each pixel is ed to each signature, according to a decision rule, or m. Pixels that pass the criteria that are established by the

n rule are then assigned to the class for that signature. Image is for ArcGIS enables you to classify the data parametrically tistical representation.

metric rules

Analysis for ArcGIS provides these commonly-used n rules for parametric signatures:

nimum distancehalanobis distanceximum likelihood (with Bayesian variation)

arametric rule

rallelepiped

Minimum distance

The minimum distance decision rule (also called calculates the spectral distance between the measuthe candidate pixel and the mean vector for each

In this illustration, spectral distance is illustratedthe candidate pixel to the means of the three signcandidate pixel is assigned to the class with the c

µB3

µB2

µB1

µA1 µA2 µA3

◆

◆

◆

µ1

µ2

µ3


Ban

d B

data

file

val

ues

candidate pixel

oo

UNDE 141

Whe

nicXµ

S

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Whepossiclass

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D

d)

e candidate pixelle of class ccandidate pixel is s to 1.0, or is

pixels in the

algebra)bra)

x algebra)

es that the s. If this is not the lepiped or g a first-pass

ance, except that riance and ighly varied lead ample, when

pixels vary r from the mean ot a highly varied


nce decision rule.

aximum likelihood decision rule is based on the probability pixel belongs to a particular class. The basic equation es that these probabilities are equal for all classes, and that put bands have normal distributions.

quation for the Maximum Likelihood/Bayesian Classifier is llows:

ac( ) 0.5 Covc( )ln[ ]– 0.5 X Mc–( )T Covc1–( ) X Mc–( )[ ]–ln=

minimum distance decision rule, or by performinparallelepiped classification.

Mahalanobis distance is similar to minimum distthe covariance matrix is used in the equation. Vacovariance are figured in so that clusters that are hto similarly varied classes, and vice versa. For exclassifying urban areas—typically a class whosewidely—correctly classified pixels may be farthethan those of a class for water, which is usually nclass (Swain and Davis 1978).

re:

= number of bands (dimensions)= a particular band= a particular class

xyi = data file value of pixel x,y in band i

ci = mean of data file values in band i for the sample for class c

Dxyc = spectral distance from pixel x,y to the mean of class c

ce: Swain and Davis 1978

n spectral distance is computed for all possible values of c (all ble classes) the class of the candidate pixel is assigned to the for which SD is the lowest.

imum likelihood

: The maximum likelihood algorithm assumes that the grams of the bands of data have normal distributions. If this is he case, you may have better results with the minimum

Where:

D = weighted distance (likelihooc = a particular classX = the measurement vector of thMc = the mean vector of the sampac = percent probability that any

a member of class c (defaultentered from a priori data)

Covc = the covariance matrix of thesample of class c

|Covc| = determinant of Covc (matrixCovc-1 = inverse of Covc (matrix algeln = natural logarithm functionT = transposition function (matri

Mahalanobis distance

Note: The Mahalanobis distance algorithm assumhistograms of the bands have normal distributioncase, you may have better results with the paralle

YSIS FOR ARCGIS142

The e

Whe

DcXMC

CT

The p

Par

Imagrule adeciscompmaxi

TherWhebandclassfirst boun

USING IMAGE ANAL

ion rule, the data file values of the candidate pixel are ared to upper and lower limits which are the minimum and mum data file values of each band in the signature.

e are high and low limits for every signature in every band. n a pixel’s data file values are between the limits for every in a signature, then the pixel is assigned to that signature’s . In the case of a pixel falling into more than one class, then the class is the one assigned. When a pixel falls into no class daries, it is labeled unclassified.

quation for the Mahalanobis distance classifier is as follows:

re:

= Mahalanobis distance= a particular class= the measurement vector of the candidate pixel

c = the mean vector of the signature of class covc = the covariance matrix of the pixels in the

signature of class c

ovc-1 = inverse of Covc

= transposition function

ixel is assigned to the class, c, for which D is the lowest.

allelepiped

e Analysis for ArcGIS provides the parallelepiped decision s its nonparametric decision rule. In the parallelepiped

D X Mc–( )T Covc1–( ) X Mc–( )=

143

1Usi

to raster images u need to isolate ape file and you ewing

tools” is also an n certain pixel

IN THIS C

• Convers

• Convert

• Convert

1
ng Conversion
The Conversion feature gives you the ability to convert shape filesand raster images to shape files. This tool is very helpful when yoor highlight certain parts of a raster image or when you have a shneed to view it as a raster image. Possible applications include videforestation patterns, urban sprawl, and shore erosion.

The Image Info tool that is discussed in chapter 3 “Applying dataimportant part of Raster/Feature Conversion. The ability to assigvalues as NoData is very helpful when converting images.

HAPTER

ion

Raster to Features

Features to Raster

1

YSIS FOR ARCGIS

Conversion

144

Alwaysfeatureand vicsystemEven thhave arcloser ran accuhavinginterchof accu

Linear cells soof this,scale o

With prepreseaccuracand thethe numreprese

USING IMAGE ANAL

be aware of how the raster dataset will represent the s when converting points, polygons, or polylines to a raster, e versa. There is a trade off when working with a cell-based , and it is that even though points don't have area, cells do. ough points are represented by a single cell, that cell does ea. The smaller the cell size, the smaller the area, and thus a epresentation of the point feature. Points with area will have racy of plus or minus half the cell size. For many users

all data types in the same format and being able to use them angeably in the same language is more important than a loss racy.

Data is represented by a polyline that is also comprised of it has area even though by definition, lines do not. Because the accuracy of representation will vary according to the f the data the resolution of the raster dataset.

olygonal or areal data, problems can occur from trying to nts smooth polygon boundaries with square cells. The y of the representation is dependent on the scale of the data size of the cell. The finer the cell resolution and the greater ber of cells that represent small areas, the more accurate the

ntation.

145

Convert ing raster to features

s the Field

USING C

Duringpolygocontigucell borgroupeinput ra

When apolylinraster, pin the ifeature

When yfeatureinput rareprese

When yyou theconvergeometpolygoIn ordeyou canshould not be

ONVERSION

r no to have jagged or sharp edges to the new feature file, check Generalize Lines to smooth out the edges. You

note that regardless of what Field you pick, the category will populated on the Attribute Table after conversion.

After conversion to a shapefile using Value a

a conversion of a raster representing polygonal features to nal features, the polygons are built from groups of ous cells having the same cell values. Arcs are created from ders in the raster. Continuous cells with the same value are d together to form polygons. Cells that are NoData in the ster will not become features in the output polygon feature.

raster that represents linear features is converted to a e feature, a polyline is created from each cell in the input assing through the center of each cell. Cells that are NoData

nput raster will not become features in the output polyline .

ou convert a raster representing point features to point s, a point will be created in the output for each cell of the ster. Each point will be positioned at the center of the cell it nts. NoData cells will not be transformed into points.

ou choose Convert Raster to Features, the dialog will give choice of a Field to specify from the image in the sion. You will also be given the choice of an Output ry type so you can choose if the feature will be a point, a n, or a polyline according to the Field and data you’re using.

A raster image before conversion

YSIS FOR ARCGIS

Performing raster to feature conversion

2

1

3

4

6

USING IMAGE ANAL146

1. Click the Image Analysis dropdown arrow, point to Convert, and click Convert Raster to Features.

2. Click the Input raster dropdown arrow, or navigate to the directory where the raster image is stored.

3. Click the Field dropdown arrow and choose a Filed to use.4. Click the Output geometry type dropdown arrow, and

choose point, polygon, or polyline.5. Check or uncheck Generalize Lines according to your

preference.6. Navigate to the directory where the Output feature should


7

5

147

Convert ing features to raster

USING C

Any poconvernumeriuniqueoutput

When yfound wthe valusize yochooseof the ianalysi

Polylinlines recells arthat arethan online encconver

PolygofeatureWhen ypolygo

ONVERSION

s that are best represented by a series of connected cells. ou convert polygons, the cells are given the value of the

n found at the center of each cell.

lygons, polylines, or points from any source file can be ted to a raster. You can convert features using both string and c fields. Each unique string in a string field is assigned a value to the output raster. A field is added to the table of the raster to hold the original string value from the features.

ou convert points, cells are given the value of the points ithin each cell. Cells that do not contain a point are given e of NoData. You are given the option of specifying the cell

u want to use in the Feature to Raster dialog. You should the cell size based on several different factors: the resolution nput data, the output resolution needed to perform your s, and the need to maintain a rapid processing speed.

es are features that, at certain resolutions, only appear as presenting streams or roads. When you convert polylines, e given the value of the line that intersects each cell. Cells not intersected by a line are given the value NoData. If more e line is found in a cell, the cell is given the value of the first ountered while processing. Using a smaller cell size during

sion will alleviate this.

ns are used for buildings, forests, fields, and many other

YSIS FOR ARCGIS

Performing Feature to Raster conversion

3

2

1

5

USING IMAGE ANAL148

1. Click the Image Analysis dropdown arrow, point to Convert, and click Convert Feature to Raster.

2. Click the Input features dropdown arrow, or navigate to the directory where the file is stored.

3. Click the Field dropdown arrow, and select the Field option you want to use.

4. Type the Output cell size.5. Navigate to the directory where the Output Raster should


4

6

149

12App

ess of ors and the torted, but these on a planar .

when discussing the data from

formation. Since l grid, the pixels ta values for the

in displacement collinearity

ints (GCPs). In untainous areas ccuracy is

IN THIS C

• Geocorr

• Spot Pro

• Polynom

• Rubber

• Camera

• IKONOS

• Landsat

• QuickBi

• RPC Pro

2
lying Geocorrection Tools
The tools and methods described in this chapter concern the procgeometrically correcting the distortions in images caused by senscurvature of the earth. Even images of seemingly flat areas are disimages can be corrected, or rectified, so they can be represented surface, conform to other images, and have the integrity of a map

The terms geocorrection and rectification are used synonymouslygeometric correction. Rectification is the process of transformingone grid system into another grid system using a geometric transthe pixels of a new grid may not align with the pixels of the originamust be resampled. Resampling is the process of extrapolating dapixels on the new grid from the values of the source pixels.

Orthorectification is a form of rectification that corrects for terraand can be used if there is a DEM of the study area. It is based onequations, which can be derived by using 3D Ground Control Porelatively flat areas, orthorectification is not necessary, but in mo(or on aerial photographs of buildings), where a high degree of arequired, orthorectification is recommended.

HAPTER

ection Properties

perties

ial Properties

Sheeting

Properties

Properties

Properties

rd Properties

perties

1

YSIS FOR ARCGIS

When to rect i fyr, north-south, different

ed pixels must be columns. these values are can be lost during not needed in the e image. An a rectified image.

map coordinates ojected onto the r coordinate referencing, since ap coordinates. ing only if the

ferencing, by information in

ange.

d to a particular ad radiometric

age data that is ified only if they be registered to

150

Rectifimust beimage.

• coch

• ma• de• ide

pri• cre• ov• co• ex• mo• pe

loc

Beforecoordinprojectmust beprojectthe Uniprojectarea prproject

• Hoare

• Weqacc

USING IMAGE ANAL

ion and coordinate system, the primary use for the database considered. If you are doing a government project, the

ion may be predetermined. A commonly used projection in ted States government is State Plane. Use an equal area ion for thematic or distribution maps and conformal or equal ojections for presentation maps. Before selecting a map ion, consider the following:

w large or small an area is mapped? Different projections intended for different size areas.

here on the globe is the study area? Polar regions and uatorial regions require different projections for maximum uracy.

itself, involves changing only the map coordinatethe image file. The grid of the image does not ch

Geocoded data are images that have been rectifiemap projection and pixel size, and usually have hcorrections applied. It is possible to purchase imalready geocoded. Geocoded data should be rectmust conform to a different projection system orother rectified data.

cation is necessary in cases where the pixel grid of the image changed to fit a map projection system or a reference There are several reasons for rectifying image data:

mparing pixels scene to scene in applications, such as ange detection or thermal inertiapping (day and night comparison)

veloping GIS databases for GIS modelingntifying training samples according to map coordinates or to classificationating accurate scaled photomapserlaying an image with vector data, such as ArcInfomparing images that are originally at different scalestracting accurate distance and area measurementssaicking images

rforming any other analyses requiring precise geographic ations

rectifying the data, you must determine the appropriate ate system for the database. To select the optimum map

• What is the extent of the study area? Circulaeast-west, and oblique areas may all requireprojection systems (ESRI 1992).

Disadvantages of rectif ication

During rectification, the data file values of rectifiresampled to fit into a new grid of pixel rows andAlthough some of the algorithms for calculating highly reliable, some spectral integrity of the datarectification. If map coordinates or map units areapplication, then it may be wiser not to rectify thunrectified image is more spectrally correct than

Georeferencing

Georeferencing refers to the process of assigningto image data. The image data may already be prdesired plane, but not yet referenced to the propesystem. Rectification, by definition, involves geoall map projection systems are associated with mImage to image registration involves georeferencreference image is already georeferenced. Geore

APPL 151

Geo

RectFor ea papis alrsomeprodcoordgeoreIn mmap

• t• t

This althoof La

Gro

GCPcoordof tw

• sb

• rr

The treferare nregis

fication. From the s in the image are cene. The more ification is. GCPs tion of two roads, ings. For small s or geologic edges of lakes, not be used.

can be entered in

the keyboard. in the view. With enter source age to image

a hardcopy map.

use of the data of the GCPs and

quired from GPS s from 1:24,000-m.

orted in pixels. and want the he RMS error ends on the image

YING GEOCORRECTION TOOLS

inates (or other output coordinates) are known. GCPs consist o X,Y pairs of coordinates:

ource coordinates — usually data file coordinates in the image eing rectifiedeference coordinates — the coordinates of the map or eference image to which the source image is being registered

erm map coordinates is sometimes used loosely to apply to ence coordinates and rectified coordinates. These coordinates ot limited to map coordinates. For example, in image to image tration, map coordinates are not necessary.

Acceptable RMS error is determined by the end base, the type of data being used, and the accuracyancillary data being used. For example, GCPs acshould have an accuracy of about 10 m, but GCPscale maps should have an accuracy of about 20

It is important to remember that RMS error is repTherefore, if you are rectifying Landsat TM datarectification to be accurate to within 30 meters, tshould not exceed 1.00. Acceptable accuracy deparea and the particular project.

referencing only

ification is not necessary if there is no distortion in the image. xample, if an image file is produced by scanning or digitizing er map that is in the desired projection system, then that image eady planar and does not require rectification unless there is skew or rotation of the image. Scanning or digitizing

uces images that are planar, but do not contain any map inate information. These images need only to be ferenced, which is a much simpler process than rectification.

any cases, the image header can simply be updated with new coordinate information. This involves redefining:

he map coordinate of the upper left corner of the imagehe cell size (the area represented by each pixel)

information is usually the same for each layer of an image file, ugh it could be different. For example, the cell size of band 6 ndsat TM data is different than the cell size of the other bands.

und control points

s are specific pixels in an image for which the output map

Entering GCPs

Accurate GCPs are essential for an accurate rectiGCPs, the rectified coordinates for all other pointextrapolated. Select many GCPs throughout the sdispersed the GCPs are, the more reliable the rectfor large scale imagery might include the intersecairport runways, utility corridors, towers or buildscale imagery, larger features such as urban areafeatures may be used. Landmarks that can vary (other water bodies, vegetation and so on) should

The source and reference coordinates of the GCPsthe following ways:

• They may be known a priori, and entered at • Use the mouse to select a pixel from an image

both the source and destination views open, coordinates and reference coordinates for imregistration.

• Use a digitizing tablet to register an image to

Tolerance of RMS error (RMSE)

YSIS FOR ARCGIS152

Cla

Somthe cbenethe mbe bedata may train

The

NearthemavailGeoc

USING IMAGE ANAL

ssif ication

e analysts recommend classification before rectification since lassification is then based on the original data values. Another fit is that a thematic file has only one band to rectify instead of ultiple bands of a continuous file. On the other hand, it may neficial to rectify the data first, especially when using GPS

for the GCPs. Since this data is very accurate, the classification be more accurate if the new coordinates help to locate better ing samples.

matic f i les

est neighbor is the only appropriate resampling method for atic files, which may be a drawback in some applications. The able resampling methods are discussed in detail later in orrection property dialogs.

153

Geocorrect ion property dia logs

APPLYI

The indappearsGeocorcertainseveralGeneraRubber

The Gesectionlets youDisplayUnits ifnot so iHorizooriginawill no

The Linvector portionthe edgto snapYou wiwhat youp to y

1. Can

2. ThVe

3. Ifchac

NG GEOCORRECTION TOOLS

ll need to check either Vertex, Edge, or End depending on u want to snap to in another layer. The choice is completely

ou.

lick the arrows to set the Threshold, and click the Within d Over Threshold boxes to change the link colors.e Displayed Units area shows the measurement of the rtical Units.

you have shapefiles (a vector layer) active in ArcMap, eck Vertex, Boundary, or End Point. Checking one will tivate Snap Tolerance and Snap Tolerance Units.

ividual Geocorrection Tools have their own dialog that whenever you choose a model type and click on the rection Properties button. Some of the tool dialogs offer option tabs pertaining to that specific tool, but they all have tabs in common. Every Geocorrection Tool dialog has a l tab and a Links tab, and all but Polynomial Properties and Sheeting Properties have an Elevation tab.

neral tab has a Link Coloring section, a Displayed Units , and a Link Snapping section. The Link Coloring section set a Threshold and select or change link colors. The ed Units section gives you the Horizontal and Vertical they are known. Often one will be known and the other one t may say Meters for Vertical Units and Unknown for ntal Units. Display Units does not have any effect on the l data in latitude/longitude format. The image in the view t show the changes either.

k Snapping section will only be activated when you have a layer (shapefile) active in ArcMap. The purpose of this of the tool is to allow you to snap an edge, end, or vertex to e, end, or vertex of another layer. The vector layer you want to another layer will be defined in the Link Snapping box. 2

31

YSIS FOR ARCGIS154

Lin

The Linforpointimagbetwimagupdadisplexpeyou ctab.

BefoCoor

3

2

rties at the Properties dialog

k the appropriate o use the ct that layer from

ake before

in the Layers

n list. links.

ks as you enter

. displayed in the

rt Links to Shape

2

USING IMAGE ANAL

You can proof and edit the coordinates of the linthem.

1. Click the Geocorrection Properties button 2. Click the Links tab. The coordinates will be

cell array on this tab. 3. Click inside a cell and edit the contents.4. When you are finished, you can click Expo

file and save the new shapefile.

ks tab

inks tab (this display is also called a CellArray) shows mation about the links in your image, including reference s and RMS Error. If you have already added links to your e, they will be listed under this tab. The program is interactive een the image and the Links tab, so when you add links in an e or between two images, information is automatically ted in the CellArray. You can edit and delete information ayed in the CellArray as well. For example, if you want to riment with coordinates other than the ones you’ve been given, an plug your own coordinates into the CellArray on the Links

re adding links or editing the links table, you need to select the dinate System in which you want to store the link coordinates.

1. Right-click in the view area and click Propebottom of the popup menu. The Data Framedisplays.

2. Click the Coordinate System tab.3. If your link coordinates are predefined, clic

Predefined coordinate system. If you want tcoordinate system from a specific layer, selethe list of Layers.

There are a few additional checks you need to mproceeding.

1. Make sure that the correct layer is displayedbox on the Image Analysis toolbar.

2. Choose your Model Type from the dropdow3. Click the Add Links button to set your new

1

3

APPL 155

Ele

The Efor Ptab inwill becayourshouChoosectiUnitselevaLandof th

2

3

4


allow you to choose a file to use as an Elevation Source, use most of the time you will have an Elevation File to use as elevation source. If you do not have an Elevation File, you ld use a Constant elevation value as the elevation source. sing Constant changes the options in the Elevation Source

on to allow you to specify the Elevation Value and Elevation . The Constant value you should use is the average ground tion for the entire scene. The following examples use the sat Properties dialog, but the Elevation tab is the same on all e Model Types that allow you to specify elevation information.

vation tab

levation tab is in all Geocorrection Model Properties except olynomial and Rubber Sheeting. When you click the Elevation any of the Geocorrection Model Types, the default selection

Elevation Source File

YSIS FOR ARCGIS156

Afterwant

The fof insecon

1.2.

3.4.5.

2

3

USING IMAGE ANAL

ollowing steps take you through the Elevation tab. The first set structions pertains to using File as your Elevation Source. The d set uses Constant as the Elevation Source.

Choose File.Type the file name or navigate to the directory where the Elevation File is stored. Click the dropdown arrow and choose Feet or Meters.Check if you want to Account for the Earth’s curvature.Click Apply to set the Elevation Source. Click OK if you are finished with the dialog.

Elevation Source Constant

the Elevation Source section you can check the box if you to Account for Earth’s curvature as part of the Elevation.

1

4

APPL 157

Theseleva

1.2.3.4.5.


4

e are the steps to take when using a Constant value as the tion source.

Choose Constant.Click the arrows to enter the Elevation Value.Click the dropdown arrow, and choose either Feet or Meters.Check if you want to Account for the Earth’s curvature.Click Apply to set the Elevation Source. Click OK if you are finished with the dialog.

1

2

3

YSIS FOR ARCGIS

SPOT

l resolution, 8-bit sen 1996).

ths

ments

sponds to the e of healthy

ful for etween plant o useful for soil eological ations.

ecially e amount of ass present in a

ul for crop d emphasizes

nd/water

158

The fird’Etudsecondlauncheand panscannescanninSPOT pfrom Lorbit.

The SPevery 2but it dany poiangle. Uviewed

This ofstation,in the pdisastevery usbe extr

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Panc

SPOT P× 10 mis similresolut

USING IMAGE ANAL

acted.

dth of the swath observed varies between 60 km for nadir g and 80 km for off-nadir viewing at a height of 832 km 1996).

hromatic

anchromatic (meaning sensitive to all visible colors) has 10 spatial resolution, contains 1 band—0.51 to 0.73 mm—and ar to a black and white photograph. It has a radiometric ion of 8 bits (Jensen 1996).

scene. It is usefidentification ansoil/crop and lacontrasts.

st SPOT satellite, developed by the French Centre National es Spatiales (CNES), was launched in early 1986. The SPOT satellite was launched in 1990, and the third was d in 1993. The sensors operate in two modes, multispectral chromatic. SPOT is commonly referred to as a pushbroom

r, which means that all scanning parts are fixed, and g is accomplished by the forward motion of the scanner. ushes 3000/6000 sensors along its orbit. This is different

andsat which scans with 16 detectors perpendicular to its

OT satellite can observe the same area on the globe once 6 days. The SPOT scanner normally produces nadir views, oes have off-nadir viewing capability. Off-nadir refers to nt that is not directly beneath the detectors, but off to an sing this off-nadir capability, one area on the earth can be

as often as every 3 days.

f-nadir viewing can be programmed from the ground control and is quite useful for collecting data in a region not directly ath of the scanner or in the event of a natural or man-made

r, where timeliness of data acquisition is crucial. It is also eful in collecting stereo data from which elevation data can

XS

SPOT XS, or multispectral, has 20 × 20 m spatiaradiometric resolution, and contains 3 bands (Jen

SPOT XS Bands and Waveleng

Band Wavelength(microns) Com

1, Green 0.50 to 0.59 µm

This band corregreen reflectancvegetation.

2, Red 0.61 to 0.68 µm

This band is usediscriminating bspecies. It is alsboundary and gboundary deline

3, Reflective IR

0.79 to 0.89 µm

This band is espresponsive to thvegetation biom

APPL 159

Ste

TwosucceeitheSteresceneprod(Jens

Toporepre1990

radioreso

0-

T 4 carries High ts that obtain tral bands.

bove the Equator. multispectral tral scanner has a km. The m, and a swath

ths

gth

9 µm

8 µm

9 µm

5 µm

8 µm


observations can be made by the panchromatic scanner on ssive days, so that the two images are acquired at angles on

r side of the vertical, resulting in stereoscopic imagery. oscopic imagery can also be achieved by using one vertical and one off-nadir scene. This type of imagery can be used to

uce a single image, or topographic and planimetric maps en 1996).

graphic maps indicate elevation. Planimetric maps correctly sent horizontal distances between objects (Star and Estes ).

Panchromatic 0.61 to 0.6

SPOT Panchromatic versus SPOT XS

reoscopic pairs

Panchromatic

XS

1 band

3 bands

1 pixel =10 m x 10 m


metriclution255

SPOT 4

The SPOT 4 satellite was launched in 1998. SPOResolution Visible Infrared (HR VIR) instrumeninformation in the visible and near-infrared spec

The SPOT 4 satellite orbits the earth at 822 km aThe SPOT 4 satellite has two sensors on board: asensor, and a panchromatic sensor. The multispecpixel size of 20 × 20 m, and a swath width of 60 panchromatic scanner has a pixel size of 10 × 10width of 60 km.

SPOT 4 Bands and Waveleng

Band Wavelen

1, Green 0.50 to 0.5

2, Red 0.61 to 0.6

3, (near-IR) 0.78 to 0.8

4, (mid-IR) 1.58 to 1.7

YSIS FOR ARCGIS

The Spot Propert ies dia log

6

160

In addiPropertGeocoreach on

1. C2. C3. C4. C5. C6. C7. C

la8. C

USING IMAGE ANAL

8

tion to the General, Links, and Elevation tabs, the Spot ies dialog also contains a Parameters tab. Most of the rection Properties dialogs do contain a Parameters tab, but e offers different options.

lick the Model Types dropdown arrow, and choose Spot.lick the Geocorrection Properties button.lick the Parameters tab on the Spot Properties dialog.hoose the Sensor type.lick the arrows to enter the Number of Iterations.lick the arrows to enter the Incidence Angle.lick the arrows to enter the Background Value, and the yer.lick OK.

1

4

7

5

2

3

161

Polynomial t ransformat ionere isn’t a perfect cients represent. ate and the curve his chapter in

ion. It can change:

aw imagery to a rojection to ively small image tions to an image ix itself. Linear g GCPs on the at TM data, rotate clination stated in rth is up.

ta that are already andsat Level 1B

not be rectified to of rectification, it ation if at first a

rst, such as the atic errors.

rmation consists and Y).

APPLYI

Polynorectifieimagerthe neepolynoof transtransfoorder trtransfo

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Source X coordinate

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ce X

coo

rdin

ate

GCP

Polynomial curve

scanned quad sheets according to the angle of dethe legend, and rotate descending data so that no

A 1st order transformation can also be used for daprojected onto a plane. For example, SPOT and Ldata are already transformed to a plane, but may the desired map projection. When doing this typeis not advisable to increase the order of transformhigh RMS error occurs. Examine other factors fiGCP source and distribution, and look for system

The transformation matrix for a 1st-order transfoof six coefficients—three for each coordinate (X

mial equations are used to convert source file coordinates to d map coordinates. Depending upon the distortion in the y, complex polynomial equations may be required to express ded transformation. The degree of complexity of the mial is expressed as the order of the polynomial. The order formation is the order of the polynomial used in the rmation. Image Analysis for ArcGIS allows 1st through nth ansformations. Usually, 1st order or 2nd order rmations are used.

sformation matrix

formation matrix is computed from the GCPs. The matrix s of coefficients that are used in polynomial equations to t the coordinates. The size of the matrix depends upon the f transformation. The goal in calculating the coefficients of sformation matrix is to derive the polynomial equations for here is the least possible amount of error when they are used form the reference coordinates of the GCPs into the source ates. It is not always possible to derive coefficients that

e no error. For example, in the figure below, GCPs are on a graph and compared to the curve that is expressed by a mial.

Every GCP influences the coefficients, even if thfit of each GCP to the polynomial that the coeffiThe distance between the GCP reference coordinis called RMS error, which is discussed later in t“Camera Properties” on page 171.

Linear transformations

A 1st order transformation is a linear transformat

• location in X and/or Y• scale in X and/or Y• skew in X and/or Y• rotation

1st order transformations can be used to project rplanar map projection, to convert a planar map panother planar map projection, and to rectify relatareas. You can perform simple linear transformadisplayed in a view or to the transformation matrtransformations may be required before collectindisplayed image. You can reorient skewed Lands

YSIS FOR ARCGIS162

Coef

Whe

x andx0 anthe c

Non

Secoto a pthe ecamedistoradardisto

f order t contains

nts — one set for

reases with the

ation take this

xi j– yj×

xi j– yj×

USING IMAGE ANAL

l inear transformations

nd-order transformations can be used to convert Lat/Lon data lanar projection, for data covering a large area (to account for arth’s curvature), and with distorted data (for example, due to ra lens distortion). Third-order transformations are used with rted aerial photographs, on scans of warped maps and with imagery. Fourth-order transformations can be used on very rted aerial photographs.

xo

tΣ

i o= i

Σj o=

= ak ×

yo

tΣ

i o= i

Σj o=

= bk ×

ficients are used in a 1st order polynomial as follows:

re:

y are source coordinates (input)d y0 are rectified coordinates (output)oefficients of the transformation matrix are as above

a0 a1 a2b0 b1 b2

x0 a0 a1x a2y+ +=

y0 b0 b1x b2y+ +=

The transformation matrix for a transformation othis number of coefficients:

It is multiplied by two for the two sets of coefficieX and one for Y.

An easier way to arrive at the same number is:

Clearly, the size of the transformation matrix incorder of the transformation.

High order polynomials

The polynomial equations for a t order transformform:

2 ii 0=

t 1+

∑

t 1+( )x t 2+( )

APPL 163

Whe

t is th

a and

the s

Effe

The cmoreThercompdiffehelpf

The f(X,Yenabthat hBecanumbperfo

would generally hod. Suppose

he X coordinates, ts are in

uation of a line polynomial is so known as a n the next page:

ce X atet)


les you to draw two-dimensional graphs that illustrate the way igher orders of transformation affect the output image.

use only the X coordinate is used in these examples, the er of GCPs used is less than the number required to actually rm the different orders of transformation.

xr = the reference X coordinate

xi = the source X coordinate

This equation takes on the same format as the eq(y = mx + b). In mathematical terms, a 1st-orderlinear. Therefore, a 1st-order transformation is allinear transformation. This equation is graphed o

re:

e order of the polynomial

b are coefficients

ubscript k in a and b is determined by:

cts of order

omputation and output of a higher polynomial equation are complex than that of a lower order polynomial equation. efore, higher order polynomials are used to perform more licated image rectifications. To understand the effects of

rent orders of transformation in image rectification, it is ul to see the output of various orders of polynomials.

ollowing example uses only one coordinate (X) instead of two ) which are used in the polynomials for rectification. This

k i i j+⋅2

--------------- j+=

Coefficients like those presented in this examplebe calculated by the least squares regression metGCPs are entered with these X coordinates:

These GCPs allow a 1st order transformation of twhich is satisfied by this equation (the coefficienparentheses):

Where:

Source X Coordinate

(input)

ReferenCoordin

(outpu

1 17

2 9

3 1

xr 25( ) 8–( )xi+=

YSIS FOR ARCGIS164

How

:

tes that they ke the one above. presses these

ear. The graph of

)xi2

(2)xi2

USING IMAGE ANAL

3 1

0 1 2 3 4

0

4

8

12

16

refe

renc

e X

coo

rdin

ate

source X coordinate

xr = (31) + (-16)xi +

ever, what if the second GCP were changed as follows?

Source X Coordinate

(input)

Reference X Coordinate

(output)

1 17

2 7

0 1 2 3 4

0

4

8

12

16

refe

renc

e X

coo

rdin

ate

source X coordinate

xr = (25) + (-8)xi

These points are plotted against each other below

A line cannot connect these points, which illustracannot be expressed by a 1st-order polynomial liIn this case, a 2nd-order polynomial equation expoints.

Polynomials of the 2nd-order or higher are nonlinthis curve is drawn below:

0 1 2 3 4

0

4

8

12

16

refe

renc

e X

coo

rdin

ate

source X coordinate

xr 31( ) 16–( )xi 2(+ +=

APPL 165

Wha

As ilcurvethe G3rd-o

. However, this ing a coordinate anted distortions r all the GCPs. In y would be too ould be arranged

direction.

ce X atet)

i2 + (1)xi

3

17

7

1

5


lustrated in the graph above, this fourth GCP does not fit on the of the 2nd-order polynomial equation. To ensure that all of CPs fit, the order of the transformation could be increased to rder. The equation and graph below could then result.

0 1 2 3 4

0

ref

source X coordinate

xr 25( ) 5–( )xi 4–( )xi2 1( )xi

2+ + +=

Coordinate(input)

Coordin(outpu

1

2

3

4

x0 1( ) =

x0 2( ) =

x0 3( ) =

x0 4( ) =

t if one more GCP were added to the list?

Source X Coordinate

(input)

Reference X Coordinate

(output)

1 17

2 7

3 1

4 5

4

8

12

16

eren

ce X

coo

rdin

ate

xr = (31) + (-16)xi + (2)xi2

(4,5)

This figure illustrates a 3rd-order transformationequation may be unnecessarily complex. Performtransformation with this equation may cause unwin the output image for the sake of a perfect fit fothis example, a 3rd-order transformation probablhigh, because the output pixels in the X direction win a different order than the input pixels in the X

Source X Referen

0 1 2 3 4

0

4

8

12

16

refe

renc

e X

coo

rdin

ate

source X coordinate

xr = (25) + (-5)xi + (-4)x

YSIS FOR ARCGIS166

In thprod

1

3

orrect more a higher order of nce, three points

transformation, least three GCPs -order ix points are st six GCPs are The minimum ation of order t

henever possible. re, no matter how

USING IMAGE ANAL

is case a higher order of transformation would probably not uce the desired results.

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

4 2 1

output imageX coordinates

x0 1( ) x0 2( ) x0 4( ) x0 3( )>>>

17 7 5 1>>>

1 2 3 4

1 2 3 4

input imageX coordinates

Minimum number of GCPs

Higher orders of transformation can be used to ccomplicated types of distortion. However, to usetransformation, more GCPs are needed. For instadefine a plane. Therefore, to perform a 1st-orderwhich is expressed by the equation of a plane, atare needed. Similarly, the equation used in a 2ndtransformation is the equation of a paraboloid. Srequired to define a paraboloid. Therefore, at learequired to perform a 2nd-order transformation. number of points required to perform a transformequals:

Use more than the minimum number of GCPs wAlthough it is possible to get a perfect fit, it is ramany GCPs are used.

t 1+( ) t 2+( )( )2

-------------------------------------

APPL 167

For 1of GCfollo


9 55

10 66

st through 10th-order transformations, the minimum number Ps required to perform a transformation is listed in the

wing table:

Number of GCPs

Order of Transformation

Minimum GCPs Required

1 3

2 6

3 10

4 15

5 21

6 28

7 36

8 45

YSIS FOR ARCGIS

The Polynomial Propert ies dia log

168

PolynoGeneraGenerathe beg

The Patransfomodel

1. C2. U

USING IMAGE ANAL

mial Properties has a Parameters tab in addition to the l and Links tabs. It does not need an Elevation tab. The l tab and the Links tab are the same as the ones featured at inning of this chapter.

rameters tab contains a CellArray that shows the rmation coefficients table. These are filled in when the is solved.

lick the Parameters tab.sing the arrows, enter the Polynomial Order.

21

169

Rubber Sheet ing

e spatial order of ification can be -based method is

smaller subsets. If complicated, the modeled through

the general ion systems.

r transformation

ere are three own coefficients

APPLYI

Trian

The fincomplismall sinterporectificmany trThen, tmathemfor eacthroughelemencalled tand resby-trian

This trirectificphotog

Trian

To perftriangu(1994) arbitrarkinds, tadoptedtriangle

The Decircumof any defined


orm the triangle-based rectification, it is necessary to late the control points into a mesh of triangles. Watson summarily listed four kinds of triangulation, including the y, optimal, Greedy, and Delaunay triangulation. Of the four he Delaunay triangulation is most widely used and is because of the smaller angle variations of the resulting s.

launay triangulation can be constructed by the empty circle criterion. The circumcircle formed from three points triangle does not have any other point inside. The triangles this way are the most equiangular possible.

There is no need for extra information because thknown conditions in each triangle and three unknfor each polynomial.

0 1 2

gle-based f inite element analysis

ite element analysis is a powerful tool for solving cated computation problems which can be approached by impler pieces. It has been widely used as a local lation technique in geographic applications. For image ation, the known control points can be triangulated into iangles. Each triangle has three control points as its vertices. he polynomial transformation can be used to establish atical relationships between source and destination systems

h triangle. Because the transformation exactly passes each control point and is not in a uniform manner, finite t analysis is also called Rubber Sheeting. It can also be he triangle-based rectification because the transformation ampling for image rectification are performed on a triangle-gle basis.

angle-based technique should be used when other ation methods such as Polynomial Transformation and rammetric modeling cannot produce acceptable results.

gulation

Triangle-based rectif ication

Once the triangle mesh has been generated and ththe control points is available, the geometric rectdone on a triangle-by-triangle basis. This triangleappealing because it breaks the entire region into the geometric problem of the entire region is verygeometry of each subset can be much simpler andsimple transformation.

For each triangle, the polynomials can be used astransformation form between source and destinat

Linear transformation

The easiest and fastest transformation is the lineawith the first order polynomials:

xo a0 a1x a2y+ +=

yo b b x b y+ +=

YSIS FOR ARCGIS170

Non

Evendisadsmoolinesrubbthe cdistritranscons

The fas thsmooparti(Aki

mial to be ditions should be nt value is given, ivatives can be mial using the total 18 itions can be

ivative on each means that the er in the normal

kpoint analysis is eeting modeling. ng, the ground cess, do not have

e the geometric ordinate systems, kpoints is

USING IMAGE ANAL

j 0=i 0=

y0 bk xi j– yj⋅ ⋅j 0=

i

∑i 0=

5

∑=

l inear transformation

though the linear transformation is easy and fast, it has one vantage. The transitions between triangles are not always th. This phenomenon is obvious when shaded relief or contour

are derived from the DEM which is generated by the linear er sheeting. It is caused by incorporating the slope change of ontrol data at the triangle edges and vertices. In order to bute the slope change smoothly across triangles, the nonlinear formation with polynomial order larger than one is used by idering the gradient information.

ifth order or quintic polynomial transformation is chosen here e nonlinear rubber sheeting technique in this example. It is a th function. The transformation function and its first order

al derivative are continuous. It is not difficult to construct ma 1978). The formulation is simply as follows:

x0 ak xi j– y⋅j

⋅i

∑5

∑=

The 5th-order has 21 coefficients for each polynodetermined. For solving these unknowns, 21 conavailable. For each vertex of the triangle, one poiand two 1st-order and three 2nd-order partial dereasily derived by establishing a 2nd-order polynovertices in the neighborhood of the vertex. Then conditions are ready to be used. Three more condobtained by assuming that the normal partial deredge of the triangle is a cubic polynomial, whichsum of the polynomial items beyond the 3rd-ordpartial derivative has a value zero.

Checkpoint analysis

It should be emphasized that the independent checcritical for determining the accuracy of rubber shFor an exact modeling method like rubber sheeticontrol points, which are used in the modeling promuch geometric residuals remaining. To evaluattransformation between source and destination cothe accuracy assessment using independent checrecommended.

171

Camera Propert iesick the dropdown

s unknowntion angled-axis of the

y-axis (after

d the z-axis

rs and allows you ates. You can

ate is unknownund coordinateefinedve centerve centere center

APPLYI

The Cacollinea cameElevatiand Fid

The Orangles Rotatiorotationthe camRotationot neemodel.tab, theEarth’sin on th


Camera Properties dialog

• Y — enter the Y coordinate of the perspecti• Z — enter the Z coordinate of the perspectiv

mera model is derived by space resection based on arity equations, and is used for rectifying any image that uses ra as its sensor. In addition to the General, Links, and on tabs, Camera Properties has tabs for Orientation, Camera, ucials.

ientation feature allows you to choose different rotation and perspective center positions for the camera. The n Angle lets you customize the Omega, Phi, and Kappa angles of the image to determine the viewing direction of era. If you can fill in all the degrees and meters for the n Angle and the Perspective Center Position, then you do d the three links you normally would need for the Camera If you are going to fill in this information on the Orientation n you will need to make sure you do not check Account for curvature on the Elevation tab. You can see the areas to fill e Orientation tab below:

Rotation offers the following options when you clarrows:

• Unknown— select when the rotation angle i• Estimated — select when estimating the rota• Fixed — select when rotation angle is define• Omega — rotation angle is roll: around the x

ground system• Phi — phi rotation angle is pitch: around the

Omega rotation)• Kappa — kappa rotation angle is yaw: aroun

rotated by Omega and Phi

The Perspective Center Position is given in meteto enter the perspective center for ground coordinchoose from the following options:

• Unknown — select when the ground coordin• Estimated — select when estimating the gro• Fixed — select when ground coordinate is d• X — enter the X coordinate of the perspecti

YSIS FOR ARCGIS172

The nwherthe Pused

You came

The lFiduimagbetwactuaphotoinforthe w

frame and visible ter on the Camera al tab after you

tion, compare the he calibration n the relationship age, the

o not use over 8 demonstrate the stances.

ft of the image

p of the image

ight of the image

ttom of the image

in the viewer.

sults in large arks for interior

int collection. If ve been defined ot correspond, the inadequate tation is used as a

USING IMAGE ANAL

Camera tab on Camera Properties dialog

can click Load or Save to open or save a file with certain ra information in it.

ast tab on the Camera Properties dialog is the Fiducials tab. cials are used to compute the transformation from data file to e coordinates. Fiducial orientation defines the relationship een the image/photo-coordinate system of a frame and the l image orientation as it appears within a view. The image/-coordinate system is defined by the camera calibration

mation. The orientation of the image is largely dependent on ay the photograph was scanned during the digitization stage.

Click to select where to place the fiducial

Selecting the inappropriate fiducial orientation reRMS errors during the measurement of fiducial morientation and errors during the automatic tie poinitial approximations for exterior orientation haand the corresponding fiducial orientation does nautomatic tie point collection capability providesresults. Ensure that the appropriate fiducial orienfunction of the image/photo-coordinate system.

ext tab on Camera Properties is also called Camera. This is e you can specify the Camera Name, the Number of Fiducials, rincipal Point, and the Focal Length for the camera that was to capture your image.

The fiducials for your image will be fixed on the in the exposure. The Fiducial information you entab will be displayed in a cell array on the Fiduciclick Apply on the Camera Properties dialog.

In order to select the appropriate fiducial orientaaxis of the photo-coordinate system (defined in treport) with the orientation of the image. Based obetween the photo-coordinate system and the imappropriate fiducial orientation can be selected. Dfiducials in an image. The following illustrationsfiducial orientation used under the various circum

Fiducial One—places the marker at the le

Fiducial Two—places the marker at the to

Fiducial Three—places the marker at the r

Fiducial Four—places the marker at the bo

173

IKONOS, QuickBird, and RPC Propert ies

S satellite, which a II rocket.

The resolution of is 13 km at nadir. izontally, and 10 ntally, and 3 m

1 kilometers. The days at 1.5 m

ths

ility to rectify r property dialogs , and Elevation

(microns)

2 µm

0 µm

9 µm

0 µm

0 µm

APPLYI

IKONOto togesame eGeocorimagesimagesNITF d

It is imthe GeopropertGeocorParamefor an Rand ent

The Pamodels


IKONOS Properties Parameters tab

rameters tab is the same in all three of these Geocorrection .

The IKONOS Properties dialog gives you the abIKONOS images from the satellite. Like the othein Geocorrection, IKONOS has a General, Linkstabs as well as Parameters and Chipping.

4, NIR 0.76 to 0.9

Panchromatic 0.45 to 0.9

S, QuickBird, and RPC Properties are sometimes referred ther as the Rational Function Models. They are virtually the xcept for the files they use. The dialogs for the three in rection Properties are identical as well. IKONOS files are captured by the IKONOS satellite. QuickBird files are captured by the QuickBird satellite. RPC Properties uses ata.

portant that you click the Add Links button before you click correction Properties button to open one of these three

y dialogs. Once you click the Add Links button and click the rection Properties button, the dialog will appear. The ters tab in IKONOS, QuickBird, and RPC Properties calls PC file and the Elevation Range. Click the Parameters tab,

er the RPC File before proceeding with anything else.

IKONOS

IKONOS images are produced from the IKONOwas launched in September of 1999 by the Athen

The resolution of the panchromatic sensor is 1 m.the multispectral scanner is 4 m. The swath widthThe accuracy without ground control is 12 m horm vertically; with ground control it is 2 m horizovertically.

IKONOS orbits at an altitude of 423 miles, or 68revisit time is 2.9 days at 1 m resolution, and 1.5resolution.

IKONOS Bands and Waveleng

Band Wavelength

1, Blue 0.45 to 0.5

2, Green 0.52 to 0.6

3, Red 0.63 to 0.6

YSIS FOR ARCGIS174

The Rof thfurthshouuse i

On thwith polynoptiosolutspeci

The 0coordordera thirto redfile).

Afterthe Cfor anchip relati

kBird, and RPC

cale and Offset or e dialog will ou choose. Scale mulas for ted on the dialog.

the full, original

USING IMAGE ANAL

was derived. This is made possible by specifying an affine onship (pixel) between the chip and the full, original image.

On the Chipping tab you are given the choice of SArbitrary Affine as your chipping parameters. Thchange depending on which chipping parameter yand Offset is the more simple of the two. The forcalculating the affine using scale and offset are lisX and Y correspond to the pixel coordinates for image.

PC file is generated by the data provider based on the position e satellite at the time of image capture. The RPCs can be er refined by using ground control points (GCPs). This file ld be located in the same directory as the image you intend to n the Geocorrection process.

e Parameters tab, there is also a check box for Refinement Polynomial Order. This is provided so you may apply omial corrections to the original rational function model. This n corrects the remaining error and refines the mathematical ion. Check the box to enable the refinement process, then fy the order by clicking the arrows.

-order results in a simple shift to both image X and Y inates. The 1st-order is an affine transformation. The 2nd- results in a second order transformation, and the 3rd-order in d order transformation. Usually, a 0 or 1st-order is sufficient uce error not addressed by the rational function model (RPC

the Parameters tab on the IKONOS Properties dialog, there is hipping tab. The Chipping process allows circulation of RPCs image chip rather than the full, original image from which the

IKONOS Properties Chipping tab

The Chipping tab is the same for IKONOS, QuicProperties.

APPL 175

The fChip

• Rv

• Rtv

• Cv

• Cfd

ialog when you and y’ (y prime), ith which you are bles are either default to the

er the Chipping ll Column Count. the appropriate al image. If the the row count of er contains the

f the full, original rresponds to the

ine dialog on the

ffine


alue. In absence of header data, this value defaults to 0.ow Scale—This value corresponds to value e, a scale factor

hat is also used in rotation. In the absence of header data, this alue defaults to 1.olumn Offset—This value corresponds to value c, an offset alue. In the absence of header data, this value defaults to 0.olumn Scale—This value corresponds to value a, a scale

actor that is also used in rotation. In the absence of header ata, this value defaults to 1.

IKONOS Chipping tab using Arbitrary A

ollowing is an example of the Scale and Offset dialog on the ping tab:

IKONOS Chipping tab using Scale and Offset

ow Offset—This value corresponds to value f, an offset

The Arbitrary Affine formulas are listed on the dchoose that option. In the formulas, x’ (x prime),correspond to the pixel coordinates in the chip wcurrently working. Values for the following variaobtained from the header data of the chip, or theypredetermined values described above. Also undtab, you’ll find a box for Full Row Count and FuFor Full Row Count, if the chip header contains data, this value is the row count of the full, originheader count is absent, this value corresponds to the chip. For Full Column Count, if the chip headappropriate data, this value is the column count oimage. If the header count is absent, the value cocolumn count of the chip.

The following is an example of the Arbitrary AffChipping tab:

YSIS FOR ARCGIS176

Qui

Quicthe Qof anthe e

The Qhas a10:30sun-sat nacapapanc450-

Just Chipto bo

When you choose ed RPC file to be Analysis for

Format Standard. ompositions with ery associated

rational function data provider f image capture. s. This file should mages you intend

g contains the me way in all

USING IMAGE ANAL

like IKONOS, QuickBird has a Parameters tab as well as a ping tab on its Properties dialog. The same information applies th tabs as is discussed in the IKONOS section.

3, Red 0.63 to 0.69 µm

4, NIR 0.76 to 0.90 µm

ckBird

kBird Properties allows you to rectify images captured with uickBird satellite. Like IKONOS, QuickBird requires the use RPC file to describe the relationship between the image and arth’s surface at the time of image capture.

uickBird satellite was launched in October of 2001. Its orbit n altitude of 450 kilometers, a 93.5 minute orbit time, and a A.M. equator crossing time. The inclination is 97.2 degrees ynchronous, and the nominal swath width is 16.5 kilometers dir. The sensor has both panchromatic and multispectral bilities. The dynamic range is 11 bits per pixel for both hromatic and multispectral. The panchromatic bandwidth is 900 nanometers. The multispectral bands are as follows:

QuickBird Bands and Wavelengths

Band Wavelength (microns)

1, Blue 0.45 to 0.52 µm

2, Green 0.52 to 0.60 µm

RPC

RPC stands for rational polynomial coefficients. it, the function allows you to specify the associatused in Geocorrection. RPC Properties in Image ArcGIS allows you to work with NITF data.

NITF stands for National Imagery Transmission NITF data is designed to pack numerous image ccomplete annotation, text attachments, and imagmetadata.

The RPC file associated with the image containspolynomial coefficients that are generated by thebased on the position of the satellite at the time oThese RPCs can be further refined by using GCPbe located in the same directory as the image or ito use in orthorectification.

Just like IKONOS and QuickBird, the RPC dialoParameters and Chipping tabs. These work the sathree model properties.

177

Landsat spectrum and are oads. These bands

n of the spectrum discrimination.

site. False color tograph where sts as they would

, vegetation .

osite. (A thematic o color, the colors or instance, roads e.

or enhance the f the useful be used are

m much like the d/emitted e-infrared,

the spectrum. TM lution than MSS.

from a height of type and health ferentiation, rock

APPLYI

The Laimage tspace rinformdisplac

Land

In 1972(NASAacquisisystemand latesatelliteoperati

LandsaLandsa

MSS

The MSapproxkm for data is vegetat

The spaIFOV (approxresolut1987).

Detecto


Landsats 1, 2, and 3, and 705 km for Landsats 4 and 5. MSS widely used for general geologic studies as well as ion inventories.

tial resolution of MSS data is 56 × 79 m, with a 79 × 79 m instantaneous field of view). A typical scene contains imately 2340 rows and 3240 columns. The radiometric ion is 6-bit, but it is stored as 8-bit (Lillesand and Kiefer

rs record electromagnetic radiation (EMR) in four bands:

The TM scanner is a multispectral scanning systeMSS, except that the TM sensor records reflecteelectromagnetic energy from the visible, reflectivmiddle-infrared, and thermal-infrared regions of has higher spatial, spectral, and radiometric reso

TM has a swath width of approximately 185 km approximately 705 km. It is useful for vegetationdetermination, soil moisture, snow and cloud diftype discrimination, and so on.

ndsat dialog is used for orthorectification of any Landsat hat uses TM or MSS as its sensor. The model is derived by esection based on collinearity equations. The elevation ation is required in the model for removing relief ement.

sat 1-5

, the National Aeronautics and Space Administration ) initiated the first civilian program specializing in the tion of remotely sensed digital satellite data. The first was called ERTS (Earth Resources Technology Satellites), r renamed to Landsat. There have been several Landsat s launched since 1972. Landsats 1, 2, and 3 are no longer

ng, but Landsats 4 and 5 are still in orbit gathering data.

ts 1, 2, and 3 gathered Multispectral Scanner (MSS) data and ts 4 and 5 collect MSS and TM data.

S from Landsats 4 and 5 has a swath width of imately 185 × 170 km from a height of approximately 900

• Bands 1 and 2 are in the visible portion of theuseful in detecting cultural features, such as ralso show detail in water.

• Bands 3 and 4 are in the near-infrared portioand can be used in land/water and vegetation

• Bands 4, 3, and 2 create a false color compocomposites appear similar to an infrared phoobjects do not have the same colors or contranaturally. For instance, in an infrared imageappears red, water appears navy or black, etc

• Bands 5, 4, and 2 create a pseudo color compimage is also a pseudo color image.) In pseuddo not reflect the features in natural colors. Fmay be red, water yellow, and vegetation blu

Different color schemes can be used to bring outfeatures under study. These are by no means all ocombinations of these seven bands. The bands todetermined by the particular application.

TM

YSIS FOR ARCGIS178

The sthe thThe lstrento mmeanto 25

Dete

• Bab

• Bs

• Bt

s

nts

water areas, n soil and

e mapping, and tures.

een reflectance . Also useful for fication.

tween many o useful for dary and

delineations as es.

to the amount s present in a crop phasizes soil/ontrasts.

USING IMAGE ANAL

identification and emcrop and land/water c

patial resolution of TM is 28.5 × 28.5 m for all bands except ermal (band 6), which has a spatial resolution of 120 × 120 m. arger pixel size of this band is necessary for adequate signal gth. However, the thermal band is resampled to 28.5 × 28.5 m atch the other bands. The radiometric resolution is 8-bit, ing that each pixel has a possible range of data values from 0 5.

ctors record EMR in seven bands:

ands 1, 2, and 3 are in the visible portion of the spectrum and re useful in detecting cultural features such as roads. These ands also show detail in water.ands 4, 5, and 7 are in the reflective-infrared portion of the

pectrum and can be used in land/water discrimination.and 6 is in the thermal portion of the spectrum and is used for

hermal mapping (Jensen 1996; Lillesand and Kiefer 1987).

TM Bands and Wavelength

BandWave-length

(microns)Comme

1, Blue 0.45 to 0.52 µm

For mapping coastal differentiating betweevegetation, forest typdetecting cultural fea

2, Green

0.52 to 0.60 µm

Corresponds to the grof healthy vegetationcultural feature identi

3, Red 0.63 to 0.69 µm

For discriminating beplant species. It is alsdetermining soil boungeological boundary well as cultural featur

4, NIR 0.76 to 0.90 µm

Especially responsiveof vegetation biomasscene. It is useful for

APPL 179

5, M

6, T

7, M

Ba

ng TM Data

isplayed to create ds corresponds to e monitor. The isplay images:

True color means eye—similar to a

False color tograph where sts as they would

, vegetation .

3 bands

7 bandsM


different composite effects. The order of the banthe Red, Green, and Blue (RGB) color guns of thfollowing combinations are commonly used to d

• Bands 3, 2, 1 create a true color composite. that objects look as they would to the naked color photograph.

• Bands 4, 3, 2 create a false color composite.composites appear similar to an infrared phoobjects do not have the same colors or contranaturally. For instance, in an infrared imageappears red, water appears navy or black, etc

IR 1.55 to 1.75 µm

Sensitive to the amount of water in plants, which is useful in crop drought studies and in plant health analyses. This is also one of the few bands that can be used to discriminate between clouds, snow, and ice.

IR 10.40 to 12.50 µm

For vegetation and crop stress detection, heat intensity, insecticide applications, and for locating thermal pollution. It can also be used to locate geothermal activity.

IR 2.08 to 2.35 µm

Important for the discrimination of geologic rock type and soil boundaries, as well as soil and vegetation moisture content.

ndWave-length

(microns)Comments

Landsat MSS vs. Landsat TM

Band Combinations for Displayi

Different combinations of the TM bands can be d

radiometricresolution0-127

radiometricresolution0- 255



MSS

T

YSIS FOR ARCGIS180

• Bidm

Diffefeatucombdeter

Lan

The LMapLand

• 1• 5• 6

The pFallsdata Mbpobstrtime are otrack

wse data. Browse image location, f data is metadata, his information is g received by the s the data to Level n and band orrected, is also

resolution of the following table:

Resolution(m)

30

30

30

30

30

60

30

15

USING IMAGE ANAL

s. Landsat 7 is capable of capturing scenes without cloud uction, and the receiving stations can obtain this data in real using the X-band. Stations located around the globe, however, nly able to receive data for the portion of the ETM+ ground where the satellite can be seen by the receiving station.

2 0.52 to 0.60 µm

3 0.63 to 0.69 µm

4 0.76 to 0.90 µm

5 1.55 to 1.75 µm

6 10.4 to 12.5 µm

7 2.08 to 2.35 µm

Panchromatic (8) 0.50 to 0.90 µm

ands 5, 4, 2 create a pseudo color composite. (A thematic mage is also a pseudo color image.) In pseudo color, the colors o not reflect the features in natural colors. For instance, roads ay be red, water yellow, and vegetation blue.

rent color schemes can be used to bring out or enhance the res under study. These are by no means all of the useful inations of these seven bands. The bands to be used are mined by the particular application.

dsat 7

andsat 7 satellite, launched in 1999, uses Enhanced Thematic per Plus (ETM+) to observe the earth. The capabilities new to sat 7 include the following:

5 m spatial resolution panchromatic band% radiometric calibration with full aperture0 m spatial resolution thermal IR channel

rimary receiving station for Landsat 7 data is located in Sioux , South Dakota at the USGS EROS Data Center (EDC). ETM+ is transmitted using X-band direct downlink at a rate of 150

Landsat 7 data types

One type of data available from Landsat 7 is brodata is a lower resolution image for determining quality and information content. The other type owhich is descriptive information on the image. Tavailable via the internet within 24 hours of beinprimary ground station. Moreover, EDC processe0r. This data has been corrected for scan directioalignment errors only. Level 1G data, which is cavailable.

Landsat 7 specif ications

Information about the spectral range and groundbands of the Landsat 7 satellite is provided in the

Landsat 7 Characteristics

BandNumber

Wavelength(microns)

1 0.45 to 0.52 µm

APPL 181

Landinterkilom

The

The LGenechaponesselecCovequad


sat 7 has a swath width of 185 kilometers. The repeat coverage val is 16 days, or 233 orbits. The satellite orbits the earth at 705 eters.

Landsat dialog

andsat Properties dialog in Geocorrection Properties has the ral, Links, and Elevation tabs already discussed in this ter. It also has a Parameters tab, which is different from the discussed so far. The Parameters tab has areas where you t the type of sensor used to capture your data, the Scene rage (if you choose Quarter Scene you also choose the rant), the Number of Iterations, and the Background.

YSIS FOR ARCGIS
USING IMAGE ANAL182

GlossaryGlossary

iangle. These n density, yearly

ue. Usually, the

asic punctuation,

all other cells are

n process.

sist of lines, text, geographical

Terms

abstract symbol

An annotation symbol that has a geometric shape, such as a circle, square, or trsymbols often represent amounts that vary from place to place, such as populatiorainfall, and so on.

accuracy assessment

The comparison of a classification to geographical data that is assumed to be trassumed true data is derived from ground truthing.

American Standard Code for Information Interchange (ASCII)

A basis of character sets...to convey some control codes, space, numbers, most band unaccented letters a-z and A-Z.

analysis mask

An option that uses a raster dataset in which all cells of interest have a value andno data. Analysis mask lets you perform analysis on a selected set of cells.

ancillary data

The data, other than remotely sensed data, that is used to aid in the classificatio

annotation

The explanatory material accompanying an image or a map. Annotation can conpolygons, ellipses, rectangles, legends, scale bars, and any symbol that denotesfeatures.

183

AOI

See area of interest.

YSIS FOR ARCGIS

a priori band

e electromagnetic reen, blue, near-her user-defined e original bands, imes called

window to .

tionship between

cified number of upon the bins to

ap, not just the

o detect

USING IMAGE ANAL184

Already or previously known.

area

A measurement of a surface.

area of interest

(AOI) a point, line, or polygon that is selected as a training sample or as the image area to be used in an operation.

ASCII

See American Standard Code for Information Interchange.

aspect

The orientation, or the direction that a surface faces, with respect to the directions of the compass: north, south, east, west.

attribute

The tabular information associated with a raster or vector layer.

average

The statistical mean; the sum of a set of values divided by the number of values in the set.

A set of data file values for a specific portion of thspectrum of reflected light or emitted heat (red, ginfrared, infrared, thermal, and so on) or some otinformation created by combining or enhancing thor creating new bands from other sources. Sometchannel.

bilinear interpolation

Uses the data file values of four pixels in a 2 × 2calculate an output value with a bilinear function

bin function

A mathematical function that establishes the reladata file values and rows in a descriptor table.

bins

Ordered sets of pixels. Pixels are sorted into a spebins. The pixels are then given new values basedwhich they are assigned.

border

On a map, a line that usually encloses the entire mimage area as does a neatline.

boundary

A neighborhood analysis technique that is used tboundaries between thematic classes.

185

brightness value cell size

ap units. For area 30’ × 30’ on

rify the degree of

asses through

that share some ssification of a

a pixel as

s raster image to

reference pixels, knowledge of the ame pixels, from

GLOSSARY

The quantity of a primary color (red, green, blue) to be output to a pixel on the display device. Also called intensity value, function memory value, pixel value, display value, and screen value.

buffer zone

A specific area around a feature that is isolated for or from further analysis. For example, buffer zones are often generated around streams in site assessment studies so that further analyses exclude these areas that are often unsuitable for development.

Cartesian

A coordinate system in which data are organized on a grid and points on the grid are referenced by their X,Y coordinates.

camera properties

Camera properties are for the orthorectification of any image that uses a camera for its sensor. The model is derived by space resection based on collinearity equations. The elevation information is required in the model for removing relief displacement.

categorize

The process of choosing distinct classes to divide your image into.

cell

1. A 1 × 1 area of coverage. DTED (Digital Terrain Elevation Data) are distributed in cells. 2. A pixel; grid cell.

The area that one pixel represents, measured in mexample, one cell in the image may represent an the ground. Sometimes called the pixel size.

checkpoint analysis

The act of using check points to independently veaccuracy of a triangulation.

circumcircle

A triangle’s circumscribed circle; the circle that peach of the triangle’s three vertices.

class

A set of pixels in a GIS file that represents areas condition. Classes are usually formed through clacontinuous raster layer.

class value

A data file value of a thematic file that identifiesbelonging to a particular class.

classification

The process of assigning the pixels of a continuoudiscrete categories.

classification accuracy table

For accuracy assessment, a list of known values ofsupported by some ground truth or other a priori true class, and a list of the classified values of the sa classified file to be tested.

YSIS FOR ARCGIS

classification scheme (or classification system) continuous data

ring a s, such as on).

nother range, tretching is often the range of data ge of brightness

ss an image. Used an image.

alue of each pixel lar way. The e toward

onal coordinate row, also called

r to accept or threshold ranging

USING IMAGE ANAL186

A set of target classes. The purpose of such a scheme is to provide a framework for organizing and categorizing the information that can be extracted from the data.

clustering

Unsupervised training; the process of generating signatures based on the natural groupings of pixels in image data when they are plotted in spectral space.

clusters

The natural groupings of pixels when plotted in spectral space.

coefficient

One number in a matrix, or a constant in a polynomial expression.

collinearity

A nonlinear mathematical model that photogrammetric triangulation is based upon. Collinearity equations describe the relationship among image coordinates, ground coordinates, and orientation parameters.

contiguity analysis

A study of the ways in which pixels of a class are grouped together spatially. Groups of contiguous pixels in the same class, called raster regions, or clumps, can be identified by their sizes and multiplied.

continuous

A term used to describe raster data layers that contain quantitative and related values. See continuous data.

A type of raster data that are quantitative (measucharacteristic) and have related, continuous valueremotely sensed images ( Landsat, SPOT, and so

contrast stretch

The process of reassigning a range of values to ausually according to a linear function. Contrast sused in displaying continuous raster layers, sincefile values is usually much narrower than the ranvalues on the display device.

convolution filtering

The process of averaging small sets of pixels acroto change the spatial frequency characteristics of

convolution kernel

A matrix of numbers that is used to average the vwith the values of surrounding pixels in a particunumbers in the matrix serve to weight this averagparticular pixels.

coordinate system

A method of expressing location. In two-dimensisystems, locations are expressed by a column andX and Y.

correlation threshold

A value used in rectification to determine whethediscard GCPs. The threshold is an absolute value from 0.000 to 1.000.

187

correlation windows database

ular information. , dBASE, Oracle,

sent an image.

lue, image file

image data after s used to process stics.

he number of ixel in a user-

represent 1:24,000 and n analysis

GLOSSARY

Windows that consist of a local neighborhood of pixels.

corresponding GCPs

The GCPs that are located in the same geographic location as the selected GCPs, but are selected in different files.

covariance

Measures the tendencies of data file values for the same pixel, but in different bands, to vary with each other in relation to the means of their respective bands. These bands must be linear. Covariance is defined as the average product of the differences between the data file values in each band and the mean of each band.

covariance matrix

A square matrix that contains all of the variances and covariances within the bands in a data file.

cubic convolution

Uses the data file values of sixteen pixels in a 4 × 4 window to calculate an output with cubic function.

data

1. In the context of remote sensing, a computer file containing numbers that represent a remotely sensed image, and can be processed to display that image. 2. A collection of numbers, strings, or facts that requires some processing before it is meaningful.

A relational data structure usually used to store tabExamples of popular databases include SYBASEINFO, etc.

data file

A computer file that contains numbers that repre

data file value

Each number in an image file. Also called file vavalue, DN, brightness value, pixel.

decision rule

An equation or algorithm that is used to classify signatures have been created. The decision rule ithe data file values based upon the signature stati

density

A neighborhood analysis technique that outputs tpixels that have the same value as the analyzed pspecified window.

digital elevation model (DEM)

Continuous raster layers in which data file valueselevation. DEMs are available from the USGS at1:250,000 scale, and can be produced with terraiprograms.

YSIS FOR ARCGIS

digital terrain model (DTM) enhancement

le for a particular atures of raw, man eye.

t usually identify

he area of the

ing various types otely-sensed

bjects on the ges.

s (such as an

USING IMAGE ANAL188

A discrete expression of topography in a data array, consisting of a group of planimetric coordinates (X,Y) and the elevations of the ground points and breaklines.

dimensionality

In classification dimensionality refers to the number of layers being classified. For example, a data file with three layers is said to be three dimensional.

divergence

A statistical measure of distance between two or more signatures. Divergence can be calculated for any combination of bands used in the classification; bands that diminish the results of the classification can be ruled out.

diversity

A neighborhood analysis technique that outputs the number of different values within a user-specified window.

edge detector

A convolution kernel, which is usually a zero-sum kernel, that smooths out or zeros out areas of low spatial frequency and creates a sharp contrast where spatial frequency is high. High spatial frequency is at the edges between homogeneous groups of pixels.

edge enhancer

A high-frequency convolution kernel that brings out the edges between homogeneous groups of pixels. Unlike an edge detector, it only highlights edges, it does not necessarily eliminate other features.

The process of making an image more interpretabapplication. Enhancement can make important feremotely sensed data more interpretable to the hu

extension

The three letters after the period in a file name thathe type of file.

extent

1. The image area to be displayed in a View. 2. Tearth’s surface to be mapped.

feature collection

The process of identifying, delineating, and labelof natural and human-made phenomena from remimages.

feature extraction

The process of studying and locating areas and oground and deriving useful information from ima

feature space

An abstract space that is defined by spectral unitamount of electromagnetic radiation).

fiducial center

The center of an aerial photo.

189

fiducials geographic information system (GIS)

ation that stores, raphic data to clude computer ther data needed an knowledge.

anning and

ge data and e map projection

map coordinates re used for tifying an image.

equency of an

ber of pixels that and of data, the f all possible data ls that have each

GLOSSARY

Four or eight reference markers fixed on the frame of an aerial metric camera and visible in each exposure that are used to compute the transformation from data file to image coordinates.

file coordinates

The location of a pixel within the file in x.y coordinates. The upper left file coordinate is usually 0,0.

filtering

The removal of spatial or spectral features for data enhancement. Convolution filtering is one method of spatial filtering. Some texts may use the terms filtering and spatial filtering synonymously.

focal

The process of performing one of several analyses on data values in an image file, using a process similar to convolution filtering.

GCP matching

For image to image rectification, a GCP selected in one image is precisely matched to its counterpart in the other image using the spectral characteristics of the data and the transformation matrix.

geocorrection

The process of rectifying remotely sensed data that has distortions due to a sensor or the curvature of the earth.

A unique system designed for a particular applicenhances, combines, and analyzes layers of geogproduce interpretable information. A GIS may inimages, hardcopy maps, statistical data, and any ofor a study, as well as computer software and humGISs are used for solving complex geographic plmanagement problems.

georeferencing

The process of assigning map coordinates to imaresampling the pixels of the image to conform to thgrid.

ground control point (GCP)

Specific pixel in image data for which the output(or other output coordinates) are known. GCPs acomputing a transformation matrix, for use in rec

high frequency kernel

A convolution kernel that increases the spatial frimage. Also called a high-pass kernel.

histogram

A graph of data distribution, or a chart of the numhave each possible data file value. For a single bhorizontal axis of a histogram graph is the range ofile values. The vertical axis is a measure of pixedata value.

YSIS FOR ARCGIS

histogram equalization image matching

e points on the

(but not limited operations.

hematically

.

t areas in the he areas in the land polygons

nalysis

e as in the els, redefines the

t the spectral

SS and TM

USING IMAGE ANAL190

The process of redistributing pixel values so that there are approximately the same number of pixels with each value within a range. The result is a nearly flat histogram.

histogram matching

The process of determining a lookup table that converts the histogram of one band of an image or one color gun to resemble another histogram.

hue

A component of IHS (intensity, hue, saturation) that is representative of the color or dominant wavelength of the pixel. It varies from 0 to 360. Blue = 0 (and 360) magenta = 60, red = 120, yellow = 180, green = 240, and cyan = 300.

IKONOS properties

Use the IKONOS Properties geocorrection dialog to perform orthorectification on images gathered with the IKONOS satellite. The IKONOS satellite orbits at an altitude of 423 miles, or 681 kilometers. The revisit time is 2.9 days at 1 meter resolution, and 1.5 days at 1.5 meter resolution.

image data

Digital representations of the earth that can be used in computer image processing and GIS analyses.

image file

A file containing raster image data.

The automatic acquisition of corresponding imagoverlapping area of two images.

image processing

The manipulation of digital image data, includingto) enhancement, classification, and rectification

indices

The process used to create output images by matcombining the DN values of different bands.

IR

Infrared portion of the electromagnetic spectrum

island polygons

When using Seed Tool, island polygons represenpolygon that have differing characteristics from tlarger polygon. You have the option to use the isfeature or to turn it off when using Seed Tool.

ISODATA (Iterative Self-Organizing Data ATechnique)

A method of clustering that uses spectral distancsequential method, but iteratively classifies the pixcriteria for each class, and classifies again so thadistance patterns in the data gradually emerge.

Landsat

A series of earth-orbiting satellites that gather Mimagery operated by EOSAT.

191

layer majority

he most common indow.

spherical surface l map projections an easily

he greatest value .

bility that a pixel ssumes that these input bands have

es divided by the nalysis technique s in a user-

qual number of . A neighborhood of the data file

GLOSSARY

1. A band or channel of data. 2. A single band or set of three bands displayed using the red, green, and blue color guns. 3. A component of a GIS database that contains all of the data for one theme. A layer consists of a thematic image file, and may also include attributes.

linear

A description of a function that can be graphed as a straight line or a series of lines. Linear equations (transformations) can generally be expressed in the form of the equation of a line or plane. Also called 1st-order.

linear contrast stretch

An enhancement technique that outputs new values at regular intervals.

linear transformation

A 1st-order rectification. A linear transformation can change location in X and/or Y, scale in X and/or Y, skew in X and/or Y, and rotation.

lookup table (LUT)

An ordered set of numbers that is used to perform a function on a set of input values. To display or print an image, lookup tables translate data file values into brightness values.

low frequency kernel

A convolution kernel that decreases spatial frequency. Also called low-pass kernel.

A neighborhood analysis technique that outputs tvalue of the data file values in a user-specified w

map projection

A method of representing the three-dimensional of a planet on a two-dimensional map surface. Alinvolve the transfer of latitude and longitude ontoflattened surface.

maximum

A neighborhood analysis technique that outputs tof the data file values in a user-specified window

maximum likelihood

A classification decision rule based on the probabelongs to a particular class. The basic equation aprobabilities are equal for all classes, and that thenormal distributions.

mean

1. The statistical Average; the sum of a set of valunumber of values in the set. 2. A neighborhood athat outputs the mean value of the data file valuespecified window.

median

1. The central value in a set of data such that an evalues are greater than and less than the median. 2analysis technique that outputs the median valuevalues in a user-specified window.

YSIS FOR ARCGIS

minimum multispectral scanner (MSS)

a spatial

r’s detectors.

value is equal to retransformed

nding pixels into canning.

not want to ng pixel values eoreference to

y even if they are given are

r edge detection. parately with the

USING IMAGE ANAL192

A neighborhood analysis technique that outputs the least value of the data file values in a user-specified window.

minimum distance

A classification decision rule that calculates the spectral distance between the measurement vector for each candidate pixel and the mean vector for each signature. Also called spectral distance.

minority

A neighborhood analysis technique that outputs the least common value of the data file values in a user-specified window.

modeling

The process of creating new layers from combining or operating upon existing layers. Modeling allows the creation of new classes from existing classes and the creation of a small set of images, or a single image, which, at a glance, contains many types of information about a scene.

mosaicking

The process of piecing together images side by side to create a larger image.

multispectral classification

The process of sorting pixels into a finite number of individual classes, or categories of data, based on data file values in multiple bands.

multispectral imagery

Satellite imagery with data recorded in two or more bands.

Landsat satellite data acquired in four bands withresolution of 57 × 79 meters.

nadir

The area on the ground directly beneath a scanne

NDVI

See Normalized Difference Vegetation Index.

nearest neighbor

A resampling method in which the output data filethe input pixel that has coordinates closest to thecoordinates of the output pixel.

neighborhood analysis

Any image processing technique that takes surrouconsideration, such as convolution filtering and s

no data

NoData is what you assign to pixel values you doinclude in a classification or function. By assigniNoData, they are not given a value. Images that gnon-rectangles need a NoData concept for displanot classified. The values that NoData pixels areunderstood to be just place holders.

non-directional

The process using the Sobel and Prewitt filters foThese filters use orthogonal kernels convolved seoriginal image, and then combined.

193

nonlinear orthorectification

splacement and le.

ning either the ut files. Overlay

layers. 2. The iginal image to

file values of the limits. 2. The when graphed as

function or data, which are

(such as mean e training sample

GLOSSARY

Describing a function that cannot be expressed as the graph of a line or in the form of the equation of a line or plane. Nonlinear equations usually contain expressions with exponents. Second-order (2nd-order) or higher-order equations and transformations are nonlinear.

nonlinear transformation

A 2nd-order or higher rectification.

nonparametric signature

A signature for classification that is based on polygons or rectangles that are defined in the feature space image for the image file. There is not statistical basis for a nonparametric signature; it is simply an area in a feature space image.

normalized difference vegetation index (NDVI)

The formula for NDVI is IR - R / IR + R, where IR stands for the infrared portion of the electromagnetic spectrum, and R stands for the red portion of the electromagnetic spectrum. NDVI finds areas of vegetation in imagery.

observation

In photogrammetric triangulation, a grouping of the image coordinates for a GCP.

off-nadir

Any point that is not directly beneath a scanner’s detectors, but off to an angle. The SPOT scanner allows off-nadir viewing.

A form of rectification that corrects for terrain dican be used if a DEM of the study area is availab

overlay

1. A function that creates a composite file contaiminimum or the maximum class values of the inpsometimes refers generically to a combination ofprocess of displaying a classified file over the orinspect the classification.

panchromatic imagery

Single-band or monochrome satellite imagery.

parallelepiped

1. A classification decision rule in which the datacandidate pixel are compared to upper and lowerlimits of a parallelepiped classification, especiallyrectangles.

parameter

1. Any variable that determines the outcome of aoperation. 2. The mean and standard deviation ofsufficient to describe a normal curve.

parametric signature

A signature that is based on statistical parametersand covariance matrix) of the pixels that are in thor cluster.

YSIS FOR ARCGIS

pattern recognition principal components analysis (PCA)

undant data to be t 1989). 2. The outputting ata to be nality of the data

pective center is ientation.

file. The profiles first pixel of the

nd scanning is er, such as the

l polynomial etween the image re. By using ification on

USING IMAGE ANAL194

The science and art of finding meaningful patterns in data, which can be extracted through classification.

piecewise linear contrast stretch

An enhancement technique used to enhance a specific portion of data by dividing the lookup table into three sections: low, middle, and high.

pixel

Abbreviated from picture element; the smallest part of a picture (image).

pixel depth

The number of bits required to store all of the data file values in a file. For example, data with a pixel depth of 8, or 8-bit data, have 256 values ranging from 0-255.

pixel size

The physical dimension of a single light-sensitive element (13 × 13 microns).

polygon

A set of closed line segments defining an area.

polynomial

A mathematical expression consisting of variables and coefficients. A coefficient is a constant that is multiplied by a variable in the expression.

1. A method of data compression that allows redcompressed into fewer bands (Jensen 1996; Fausprocess of calculating principal components and principal component bands. It allows redundant dcompacted into fewer bands (that is the dimensiois reduced).

principal point

The point in the image plane onto which the persprojected, located directly beneath the interior or

profile

A row of data file values from a DEM or DTED of DEM and DTED run south to north (that is therecord is the southernmost pixel).

pushbroom

A scanner in which all scanning parts are fixed, aaccomplished by the forward motion of the scannSPOT scanner.

QuickBird

The QuickBird model requires the use of rationacoefficients (RPCs) to describe the relationship band the earth's surface at the time of image captuQuickBird Properties, you can perform orthorectimages gathered with the QuickBird satellite

195

radar data recoding

sses.

ap projection iented so that the e.

ctified, which are coordinates for inates. Because

tion, this is not

which a source f both input oint.

which the correct ta. The reference ed.

l plane at the ch the three

GLOSSARY

The remotely sensed data that are produced when a radar transmitter emits a beam of micro or millimeter waves, the waves reflect from the surfaces they strike, and the backscattered radiation is detected by the radar system’s receiving antenna, which is tuned to the frequency of the transmitted waves.

radiometric correction

The correction of variations in data that are not caused by the object or scene being scanned, such as scanner malfunction and atmospheric interference.

radiometric enhancement

An enhancement technique that deals with the individual values of pixels in an image.

radiometric resolution

The dynamic range, or number of possible data file values, in each band. This is referred to by the number of bits into which the recorded energy is divided. See pixel depth.

rank

A neighborhood analysis technique that outputs the number of values in a user-specified window that are less than the analyzed value.

raster data

A data type in which thematic class values have the same properties as interval values, except that ratio values have a natural zero or starting point.

The assignment of new values to one or more cla

rectification

The process of making image data conform to a msystem. In many cases, the image must also be ornorth direction corresponds to the top of the imag

rectified coordinates

The coordinates of a pixel in a file that has been reextrapolated from the GCPs. Ideally, the rectifiedthe GCPs are exactly equal to the reference coordthere is often some error tolerated in the rectificaalways the case.

reference coordinates

The coordinates of the map or reference image to(input) image is being registered. GCPs consist ocoordinates and reference coordinates for each p

reference pixels

In classification accuracy assessment, pixels for GIS class is known from ground truth or other dapixels can be selected by you, or randomly select

reference plane

In a topocentric coordinate system, the tangentiacenter of the image on the earth ellipsoid, on whiperpendicular coordinate axes are defined.

YSIS FOR ARCGIS

reproject RPC properties

fficients to he earth's surface associated RPC

der or higher).

color and also

true distance on ues through a

ndsat scanner or

atically generates

iles have the .shp

USING IMAGE ANAL196

Transforms raster image data from one map projection to another.

resampling

The process of extrapolating data file values for the pixels in a new grid when data have been rectified or registered to another image.

resolution

A level of precision in data.

resolution merging

The process of sharpening a lower-resolution multiband image by merging it with a higher-resolution monochrome image.

RGB

Red, green, blue. The primary additive colors that are used on most display hardware to display imagery.

RGB clustering

A clustering method for 24-bit data (three 8-bit bands) that plots pixels in three-dimensional spectral space and divides that space into sections that are used to define clusters. The output color scheme of an RGB-clustered image resembles that of the input file.

RMS error

The distance between the input (source) location of the GCP and the retransformed location for the same GCP. RMS error is calculated with a distance equation.

The RPC Properties uses rational polynomial coedescribe the relationship between the image and tat the time of image capture. You can specify thefile to be used in your geocorrection.

rubber sheeting

The application of nonlinear rectification (2nd-or

saturation

A component of IHS that represents the purity ofvaries linearly from 0 to 1.

scale

1. The ratio of distance on a map as related to thethe ground. 2. Cell size. 3. The processing of vallookup table.

scanner

The entire data acquisition system such as the Lathe SPOT panchromatic scanner.

seed tool

An Image Analysis for ArcGIS feature that automfeature layer polygons of similar spectral value.

shapefile

A vector format that contains spatial data. Shapefextension.

197

signature spectral enhancement

based on the values of

ed by the sensor, el.

s (such as an of spectral space n techniques that onal vectors,

ltispectral and shbroom scanner, nning is er.

es which is used A neighborhood ation of the data

GLOSSARY

A set of statistics that defines a training sample or cluster. The signature is used in a classification process. Each signature corresponds to a GIS class that is created from the signatures with a classification decision rule.

source coordinates

In the rectification process, the input coordinates.

spatial enhancement

The process of modifying the values of pixels in an image relative to the pixels that surround them.

spatial frequency

The difference between the highest and lowest values of a contiguous set of pixels.

spatial resolution

A measure of the smallest object that can be resolved by the sensor, or the area on the ground represented by each pixel.

speckle noise

The light and dark pixel noise that appears in radar data.

spectral distance

The distance in spectral space computed as Euclidean distance in n-dimensions, where n is the number bands.

The process of modifying the pixels of an imageoriginal values of each pixel, independent of the surrounding pixels.

spectral resolution

A measure of the smallest object that can be resolvor the area on the ground represented by each pix

spectral space

An abstract space that is defined by spectral unitamount of electromagnetic radiation). The notionis used to describe enhancement and classificatiocompute the spectral distance between n-dimensiwhere n is the number of bands in the data.

SPOT

SPOT satellite sensors operate in two modes, mupanchromatic. SPOT is often referred to as the pumeaning that all scanning parts are fixed, and scaaccomplished by the forward motion of the scann

standard deviation

1. The square root of the variance of a set of valuas a measurement of the spread of the values. 2. analysis technique that outputs the standard devifile values of a user-specified window.

YSIS FOR ARCGIS

striping temporal resolution

ry of a particular

on data.

values that are

for ArcGIS that e same area getation, urban

ematic layers as land cover, soil

particular spatial .

l resolution of 30

USING IMAGE ANAL198

A data error that occurs if a detector on a scanning system goes out of adjustment, that is, it provides readings consistently greater than or less than the other detectors for the same band over the same ground cover.

subsetting

The process of breaking out a portion of a large image file into one or more smaller files.

sum

A neighborhood analysis technique that outputs the total of the data file values in a user-specified window.

supervised training

Any method of generating signatures for classification in which the analyst is directly involved in the pattern recognition process. Usually, supervised training requires the analyst to select training samples from the data that represent patterns to be classified.

swath width

In a satellite system, the total width of the area on the ground covered by the scanner.

summarize areas

A common workflow progression with feature theme corresponding to an area of interest to summarize the change just within a certain area.

The frequency with which a sensor obtains imagearea.

terrain analysis

The processing and graphic simulation of elevati

terrain data

Elevation data expressed as a series of x, y, and zeither regularly or irregularly spaced.

thematic change

Thematic Change is a feature in Image Analysis allows you to compare two thematic images of thcaptured at different times to notice change in veareas, and so on.

thematic data

Raster data that is qualitative and categorical. Thoften contain classes of related information, such type, slope, etc.

thematic map

A map illustrating the class characterizations of avariable such as soils, land cover, hydrology, etc

thematic mapper (TM)

Landsat data acquired in seven bands with a spatia× 30 meters.

199

theme true color

continuous raster file values and s. The image

nslated through gun.

ition in which used to uncover

presented with a band image. 4. In name using a

h as points, lines, stored, instead of

tion.

mage so they can agnification, ly, leaving image

GLOSSARY

A particular type of information, such as soil type or land use, that is represented in a layer.

threshold

A limit, or cutoff point, usually a maximum allowable amount of error in an analysis. In classification, thresholding is the process of identifying a maximum distance between a pixel and the mean of the signature to which it was classified.

training

The process of defining the criteria by which patterns in image data are recognized for the purpose of classification.

training sample

A set of pixels selected to represent a potential class. Also called sample.

transformation matrix

A set of coefficients that is computed from GCPs, and used in polynomial equations to convert coordinates from one system to another. The size of the matrix depends upon the order of the transformation.

triangulation

Establishes the geometry of the camera or sensor relative to objects on the earth’s surface.

A method of displaying an image (usually from a layer) that retains the relationships between data represents multiple bands with separate color gunmemory values from each displayed band are trathe function memory of the corresponding color

unsupervised training

A computer-automated method of pattern recognsome parameters are specified by the user and arestatistical patterns that are inherent in the data.

variable

1. A numeric value that is changeable, usually reletter. 2. A thematic layer. 3. One band of a multimodels, objects that have been associated with a declaration statement.

vector data

Data that represents physical forms (elements) sucand polygons. Only the vertices of vector data areevery point that makes up the element.

vegetative indices

A gray scale image that clearly highlights vegeta

zoom

The process of expanding displayed pixels on an ibe more closely studied. Zooming is similar to mexcept that it changes the display only temporarimemory the same.

YSIS FOR ARCGIS
USING IMAGE ANAL200

References References

ting for ftware 4(2),

a . 199: 9-19.

ge atic.” .

ifornia:

sat Radar : 349-355.

dures.

ncyclopedia illiams. New

,

itted to the

This appendix lists references used in the creation of this book.

Akima, H., 1978, A Method for Bivariate Interpolation and Smooth Surface FitIrregularly Distributed Data Points, ACM Transactions on Mathematical Sopp. 148-159.

Buchanan, M.D. 1979. “Effective Utilization of Color in Multidimensional DatPresentation. “Proceedings of the Society of Photo-Optical Engineers, Vol

Chavez, Pat S., Jr, et al. 1991. “Comparison of Three Different Methods to MerMultiresolution and Multispectral Data: Landsat TM and SPOT PanchromPhotogrammetric Engineering & Remote Sensing, Vol. 57, No. 3: 295-303

Conrac Corp., Conrac Division. 1980. Raster Graphics Handbook. Covina, CalConrac Corp.

Daily, Mike. 1983. “Hue-Saturation-Intensity Split-Spectrum Processing of SeaImagery.” Photogrammetric Engineering& Remote Sensing, Vol. 49, No. 3

ERDAS 2000. ArcView Image Analysis. Atlanta, Georgia: ERDAS, Inc.

ERDAS 1999. Field Guide. 5th ed. Atlanta: ERDAS, Inc.

ESRI 1992. Map Projections & Coordinate Management: Concepts and ProceRedlands, California: ESRI, Inc.

Faust, Nickolas L. 1989. “Image Enhancement.” Volume 20, Supplement 5 of Eof Computer Science and Technology, edited by Allen Kent and James G. WYork: Marcel Dekker, Inc.

Gonzalez, Rafael C., and Paul Wintz. 1977. Digital Image Processing. ReadingMassachusetts: Addison-Wesley Publishing Company.

Holcomb, Derrold W. 1993. “Merging Radar and VIS/IR Imagery.” Paper subm1993 ERIM Conference, Pasadena, California.

201

Hord, R. Michael. 1982. Digital Image Processing of Remotely Sensed Data. New York. Academic Press.

YSIS FOR ARCGIS

Jensen, John R., et al. 1983. “Urban/Suburban Land Use Analysis.” Chapter 30 in Manual of Remote Sensing, edited by Robert N.

ersey:

Annual

Sons, Inc.

mation

demic Press.

ntent.”

rentice-Hall.

2). West

ok Company.

Publishing

ensing of

tions, and

USING IMAGE ANAL202

Colwell. Falls Church, Virginia: American Society of Photogrammetry.

Jensen, John R. 1996. Introductory Digital Image Processing: A Remote Sensing Perspective. Englewood Cliffs, New JPrentice-Hall.

Kloer, Brian R. 1994. “Hybrid Parametric/Non-parametric Image Classification.” Paper presented at the ACSM-ASPRSConvention, April 1994, Reno, Nevada.

Lillesand, Thomas M., and Ralph W. Kiefer. 1987. Remote Sensing and Image Interpretation. New York: John Wiley &

Marble, Duane F. 1990. “Geographic Information Systems: An Overview.” Introductory Readings in Geographic InforSystems, edited by Donna J. Peuquet and Duane F. Marble. Bristol, Pennsylvania: Taylor & Francis, Inc.

McCoy, Jill, and Kevin Johnston. Using ArcGIS Spatial Analyst. Redlands, California: ESRI, Inc.

Sabins, Floyd F., Jr. 1987. Remote Sensing Principles and Interpretation. New York: W. H. Freeman and Co.

Schowengerdt, Robert A. 1983. Techniques for Image Processing and Classification in Remote Sensing. New York. Aca

Schowengerdt, Robert A. 1980. “Reconstruction of Multispatial, Multispectral Image Data Using Spatial Frequency CoPhotogrammetric Engineering & Remote Sensing, Vol. 46, No. 10: 1325-1334.

Star, Jeffrey, and John Estes. 1990. Geographic Information Systems: An Introduction. Englewood Cliffs, New Jersey: P

Swain, Philip H. 1973. Pattern Recognition: A Basis for Remote Sensing Data Analysis (LARS Information Note 11157Lafayette, Indiana: The Laboratory for Applications of Remote Sensing, Purdue University.

Swain, Philip H., and Shirley M. Davis. 1978. Remote Sensing: The Quantitative Approach. New York: McGraw Hill Bo

Tou, Julius T., and Rafael C. Gonzalez. 1974. Pattern Recognition Principles. Reading, Massachusetts: Addison-WesleyCompany.

Tucker, Compton J. 1979. “Red and Photographic Infrared Linear Combinations for Monitoring Vegetation.” Remote SEnvironment, Vol. 8: 127-150.

Walker, Terri C., and Richard K. Miller. 1990. Geographic Information Systems: An Assessment of Technology, ApplicaProducts. Madison, Georgia: SEAI Technical Publications.

Watson, David, 1994, Contouring: A Guide to the Analysis and Display of Spatial Data, Elsevier Science, New York.

203

Welch, R., and W.Ehlers. 1987. “Merging Multiresolution SPOT HRV and Landsat TM Data.” Photogrammetric Engineering &

REFERENCES

Remote Sensing, Vol. 53, No. 3: 301-303.

YSIS FOR ARCGIS
USING IMAGE ANAL204

IndexIndex

AA priori 183

Cell 185Cell Size 48Cell Size Tab

114

185

viation 85

Absorption spectra 101Abstract symbol 183Accuracy assessment 183Ancillary data 183Annotation 183AOI 183Area 184Area of interest 184ASCII 183Aspect 184Atmospheric correction 91Attribute 184Average 184AVHRR 102

BBand 184Bilinear interpolation 184Bin 87Bin function 184Bins 184Border 184Boundary 184brightness inversion 94Brightness value 184Brovey Transform 79Buffer zone 185

CCamera Model

tutorial 33Camera Properties

Fiducials 172

workflow 51Checkpoint analysis 170Class 185

valuenumbering systems

Class value 185Classification 152, 185Classification accuracy tableClassification scheme 185Clustering 186Clusters 186Coefficient 186Collinearity 186Contiguity analysis 186Continuous 186Continuous data 186Contrast stretch

for display 85linear 84min/max vs. standard denonlinear 84piecewise linear 84

Convolution 70filtering 109

Convolution Filtering 70Convolution filtering 186Convolution kernel 186Coordinate system 186Correlation threshold 186Correlation windows 186Corresponding GCPs 187Covariance 187Covariance matrix 187

205

Camera properties 185Camera Properties dialog 171Cartesian 185Categorize 185

Creating a shapefiletutorial 18

Cubic convolution 187

YSIS FOR ARCGIS

D G Chipping tab 1740g 173

108

hs 177

181

169, 191161

1

91

USING IMAGE ANAL206

Data 108, 187Data file 187Data file value 187

display 84Database 187Decision rule 187Digital elevation model 187Digital terrain model 187Display device 84, 85, 96

EEdge detector 188Edge enhancer 188Effects of order 163Enhancement 188

linear 84nonlinear 84radiometric 83spatial 83

Extension 188Extent 47Extent Tab

workflow 51

FFeature collection 188Feature extraction 188Feature space 188Fiducial center 188Fiducials 188File coordinates 189Filtering 189Finding areas of change 22Focal 189Focal Analysis 77

workflow 78Focal operation 109

GCP matching 189GCPs 151General Tab

workflow 50Geocorrection 189

tutorial 33Geocorrection property dialogs 153

Elevation tab 155General tab 153Links tab 154

Geographic information system 189Georeferencing 150, 189GIS

defined 107Ground control point 189Ground control points 151

HHigh frequency kernel 189High Frequency Kernels 72High order polynomials 162Histogram 189

breakpoint 85Histogram Equalization

tutorial 14Histogram equalization 189

formula 88Histogram match 91Histogram matching 190histogram matching 92Histogram Stretch

tutorial 14Hue 96, 190

IIdentifying similar areas 18IHS to RGB 99IKONOS

IKONOS properties 19IKONOS Properties dialoImage data 190Image Difference

tutorial 22Image file 190Image Info 45

workflow 46Image matching 190Image processing 190Index 101Indices 190Information (vs. data) Intensity 96IR 190Island Polygons 41ISODATA 190

LLandsat 190

bands and wavelengtMSS 102TM 99, 102

Landsat 7 180Landsat Properties 177Landsat Properties dialogLayer 190Linear 191Linear transformation Linear transformations Lookup table 84

display 85Lookup table (LUT) 19

MMajority 191Map projection 191Maximum likelihood 1

207

Mean 85, 191 O R

195t 195195

595195

a

6

173, 176

INDEX

Median 191Minimum 191Minimum distance 192Minimum GCPs 166Minority 192Modeling 192Mosaicking 192Mosaicking images

tutorial 30MSS 177Multispectral classification 192Multispectral imagery 192Multispectral scanner (MSS) 192

NNadir 192NDVI 192Nearest neighbor 152, 192Neighborhood analysis 109, 192

density 109diversity 109majority 109maximum 109minimum 109minority 109rank 109sum 109

NITF 176NoData Value 45Non-directional 192Non-Directional Edge 75

workflow 76Nonlinear transformation 170, 193Nonlinear transformations 162Normalized difference vegetation index

193

Observation 193Off-nadir 193Options

dialog 47Options Dialog

workflow 50Orientation tab 171Orthorectification 193

tutorial 33Overlay 193

PPanchromatic imagery 193Parallelepiped 193Parameter 193Parametric 131Parametric signature 193Pattern recognition 193Pixel 194Pixel depth 194Pixel size 194Placing links

tutorial 36Polygon 194Polynomial 194Polynomial Properties dialog 168Polynomial Transformation 161Preference Tab 51Preferences 49Principal components analysis (PCA)

194Profile 194Pushbroom 194

QQuickBird 194QuickBird Properties 176QuickBird Properties dialog 173

Radar data 194Radiometric correction Radiometric enhancemenRadiometric resolution Raster data 195Recode 114Recoding 195Rectification 150, 19Rectified coordinates 1Reference coordinates Reference pixels 195Reference plane 195Reflection spectra

see absorption spectrReproject 195Resampling 196Resolution 196

spatial 91Resolution Merge 79

workflow 80Resolution merging 19RGB 196RGB clustering 196RMS error 151, 196RMSE 35RPC properties 196RPC Properties dialog Rubber Sheeting 169Rubber sheeting 196

SSaturation 96, 196Scale 196Scanner 196Scanning window 109Seed Radius 40

workflow 44Seed Tool 18

YSIS FOR ARCGIS

controlling 40 tutorial 24

USING IMAGE ANAL208

workflow 42Seed Tool Properties 40Shadow

enhancing 84Shapefile 196Signature 196Source coordinates 197Spatial Enhancement 69Spatial enhancement 197Spatial frequency 197Spatial resolution 197Speckle noise 197Spectral distance 197Spectral enhancement 197Spectral resolution 197Spectral space 197SPOT 197

panchromatic 99XS 102

Spot 158Panchromatic 158XS 158

Spot 4 159Spot Properties dialog 160Standard deviation 85, 197Starting Image Analysis for ArcGIS 12Stereoscopic pairs 159Striping 197Subsetting 198Summarize areas 198Supervised training 198Swath width 198

TTemporal resolution 198Terrain analysis 198Terrain data 198Thematic Change

Thematic data 198Thematic files 152Thematic map 198Thematic mapper (TM) 198Theme 198Threshold 199TM 177TM data 179Training 199Training sample 199Transformation matrix 161, 199Triangle-based finite element analysis

169Triangle-based rectification 169Triangulation 169, 199True color 199tutorial 18

UUnsupervised Classification

tutorial 25Unsupervised training 199

VVariable 199Vector data 199Vegetative indices 199

ZZero Sum Kernels 72Zoom 199

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