Remote Sensing Applications Supporting Remote Sensing Applications Supporting Regional Transportation Database DevelopmentRegional Transportation Database Development

CLEM 2001CLEM 2001August 6, 2001August 6, 2001

Santa Barbara, CASanta Barbara, CA

Chris Chiesa, Chris.Chiesa@Veridian.comChris Chiesa, Chris.Chiesa@Veridian.com(520) 326-7005 ext. 106(520) 326-7005 ext. 106

Remote Sensing Application Supporting Regional Remote Sensing Application Supporting Regional

Database for Transportation PlanningDatabase for Transportation Planning

In Partnership with:In Partnership with:

Presentation OverviewPresentation Overview

Project Summary Project ObjectiveApproachBenefits

Technical DiscussionLand Cover Change Detection and MappingRoad Feature Characterization and Extraction

Project ObjectiveProject Objective

Develop tools and methods to facilitate regional transportation road network database development and maintenance

Utilize commercial remote sensing sources to identify and map changes in land use and transportation infrastructureAutomate procedure for extracting and attributing road vectorsDevelop procedures within COTS software environment (ERDAS IMAGINE / CAFÉ)

Promote awareness of tools and processes through outreach activities

Training / WorkshopsWeb-based Interactive Tutorial

Commercial Remote Sensing SourcesCommercial Remote Sensing Sources

LANDSAT Thematic Mapper

High-resolution IKONOS

ApproachApproach

1. Use multi-date Landsat Thematic Mapper imagery to identify areas within a large region where intensive urban development (hot spots) has occurred.

May 26, 1984 June 15, 2000 Urban development between 1984 and 2000

ApproachApproach

2. Acquire high-resolution (IKONOS) imagery over hot spots and enhance road network with one or more spectral features developed for the types of roads present and the geographic environment.

IKONOS panchromatic band (1-meter)

IKONOS false color composite (4-meter)

Road feature derived from linear combination of

IKONOS multi-spectral bands (4-meter)

ApproachApproach

3. Extract road locations in newly developed regions and store as vector coverage’s using Veridian’s Lines of Communication (LOC) extraction software.

ApproachApproach

4. Assign attributes (e.g. surface type, width) to vector coverages.

2-lane roads

3-lane roads

BenefitsBenefits

The LANDSAT program provides an inexpensive means of identifying landcover change over a large area.

Landsat Coverage

IKONOS Coverage

Benefits Benefits

Automated (i.e., user-assisted) road extraction using road spectral features and/or LOC toolkit can be faster, less tedious and less error prone than traditional processing of hand digitizing from aerial photography or satellite imagery.

Panchromatic Aerial Photograph Road feature derived fromMultispectral Imagery

Change Detection and Feature Change Detection and Feature Extraction ProcessExtraction Process

Change detection over a large areaRadiometric normalizationCategorize both datesCategorical changeRadiometric changeHybrid change

Feature extraction and attributionIdentify regions of intensive developmentGenerate road features.Extract road networkAttribute road network

Date 1 Geo-coded

Date 2 Geo-coded

Radiometric Normalization

Process

Date 2 Geo- coded and normalized

to Date 1

Categorical Process

Categorical Process

Date 1 Categorized

Image

Date 2 Categorized

Image

Categorical Change

Process

Categorical Change

Detection Image

Radiometric Change Detection

Process

Change Magnitude and Change

Direction

Hybrid Change Detection Process

Hybrid Change Product

Procedure OverviewProcedure Overview

Hybrid Change Detection

Radiometric Correction

Radiometric Change Detection

Categorical Change Detection

Categorical Processing

Acquire Data …Acquire Data …

Acquire 2 dates of LANDSAT dataSummer seasonCloud freeSame time of year

Mid-Michigan on June 8, 1986 Mid-Michigan on June 6, 2000

Categorize Both Dates…Categorize Both Dates…

Label resultant clusters into water, vegetation, bare ground, and urban areas, as appropriate.

Water

Vegetation

Urban

Bare ground

Unsupervised clustering of Landsat Thematic Mapper image over portion of Michigan on June 8, 1986.

Unsupervised clustering of Landsat Thematic Mapper image over portion of Michigan on June 6, 2000

Categorical Change …Categorical Change …

Recode categorized files to urban/non-urban.

Water Vegetation

UrbanBare ground

Urban

No data

Categorical Change…Categorical Change…

Combine binary files from both dates to determine where urban changes have occurred.

Date1

Date2

Urban on date 1, not on date 2

Urban on both dates

Urban on date 2, not on date 1

No data

Increasing difference between pixel values from date 1 to date 2 input images.

This change magnitude channel shows differences in two dates of Landsat imagery for a region in Michigan.

Brighter areas indicate higher magnitudes of change. Often a threshold from this channel is established so that only changes above a certain magnitude will be considered when extracting changes of interest.

Radiometric Change DetectionRadiometric Change Detection

Radiometric Change DetectionRadiometric Change Detection

The sector code channel provides information on the “direction” or nature of change. Each color corresponds to a sector code. Each sector code relates to a specific combination of changes observed in image bands as shown in the table above. For example, sector code 6, shown in orange in the image to the left, shows areas that have increased spectral reflectance in bands 2 and 3, and decreased spectral reflectance in band 4.

0

7654321

Color Sector Code

Band 2 Band 3 Band 4

Blue Near IR

RedGreen

Radiometric Change Detection…Radiometric Change Detection…

Create a change image composition (CIC) and determine sector codes that best represent urban areas.

Hybrid ChangeHybrid Change

Advantages are: 1. Labels from categorization 2. Reduction in false categorical change from CVA

Hybrid urban change product of Delta Township in Michigan. Changed areas are annotated in yellow over a Landsat Thematic Mapper False color composite

Feature Extraction and AttributionFeature Extraction and Attribution

Identify geographic locations of localized regions in LANDSAT change product where intensive development has occurredGenerate road featuresExtract road networkAttribute road network

Identify Geographic LocationsIdentify Geographic Locations

Identify areas in the Landsat hybrid change product where urban change has occurred and order IKONOS data

Order and Receive DataOrder and Receive Data

Acquire IKONOS data over area of interest

IKONOS false color composite with green band displayed in blue, red band displayed in green, and near infrared band displayed in

red.

IKONOS panchromatic bandIKONOS natural color composite with blue band displayed in blue, green band displayed in green, and red band displayed in red.

Generate Road Features…Generate Road Features…

This scatterplot illustrates how different landcover materials can be separated in 2-dimensional space (2 spectral bands). The arrow shows a direction that can be described as a linear combination of these two bands. The dashed line indicates that both concrete and asphalt can be separated from the other materials with this 2-dimensional feature. Often features are created by using multiple bands ( > than 2 dimensions)

Generate Road Features…Generate Road Features…

This plot illustrates how well a specific 4-band spectral feature will work in isolating certain landcover material from other materials in the image. Natural materials are projected towards a categorical value of 1, while man made materials are projected towards a categorical value of 2. The vertical dashed line between these two categories illustrates that this equation will work in separating these 2 categories. In the feature created, man made materials will appear as the brightest objects and natural materials will appear as darker objects. Level slicing the feature at around 150 will separate the two.

Generate Road Features…Generate Road Features…

Apply coefficients of spectral feature to data and produce road feature.

[(Band 1 * -.0256) + (Band 2 * .0915) + (Band 3 * .1346) + (Band 4 * -.2241)] + 148

Weighted average of satellite raw bands Adjusts data values into 0-255 range for

unsigned 8-bit output

Generate Road Features…Generate Road Features…

False color composite of IKONOS data displayed with green band in blue, red band in

green, and near infrared band in red

IKONOS road feature derived from a linear combination of the raw bands

Extract Road NetworkExtract Road Network

Use road feature as input to LOC toolkit and semi-automatically extract roads.Convert to vector coverage.

Extract Road Network…Extract Road Network…

Extract Road Network…Extract Road Network…

Attribute Road NetworkAttribute Road Network

2-lane roads

3-lane roads

2-lane roads

Process SummaryProcess Summary

Landsat imagery provides broad spatial and temporal coverage over which to observe land changesHybrid change detection offers advantages over traditional post-classification change detection in that it also incorporates important radiometric change information and allows “thresholding” of changesIKONOS imagery provides high spatial resolution to identify the specific transportation features that constitute the changes observed in Landsat imageryUsing a “Road Feature” helps maximize the differentiability of roads and background classes in the imagerySemi-automated extraction and labeling tools facilitate the process of developing GIS database layers from these remote sensing sources

Questions?Questions?

Please contact:Chris ChiesaVeridian Systems4400 East Broadway, Suite 116Tucson, AZ 85711

(520)326-7005 ext. 106chris.chiesa@veridian.comwww.veridian.com