Basics of digital image processingBasics of digital image processing

Lecture 4Lecture 4September 23, 2006September 23, 2006

What is image processingWhat is image processing

Is enhancing an image or extracting Is enhancing an image or extracting information or features from an imageinformation or features from an imageComputerized routines for information Computerized routines for information extraction (extraction (egeg, pattern recognition, , pattern recognition, classification) from remotely sensed classification) from remotely sensed images to obtain categories of information images to obtain categories of information about specific features.about specific features.Many moreMany more

Image Processing Includes Image Processing Includes

Image quality and statistical evaluationImage quality and statistical evaluationRadiometric correctionRadiometric correctionGeometric correctionGeometric correctionImage enhancement and sharpeningImage enhancement and sharpeningImage classificationImage classification

Pixel basedPixel basedObjectObject--oriented basedoriented based

Accuracy assessment of classificationAccuracy assessment of classificationPostPost--classification and GISclassification and GISChange detectionChange detection

Image QualityImage Quality

Many remote sensing datasets contain highMany remote sensing datasets contain high--quality, quality, accurate data. Unfortunately, sometimes error (or accurate data. Unfortunately, sometimes error (or noise) is introduced into the remote sensor data by: noise) is introduced into the remote sensor data by:

the environmentthe environment (e.g., atmospheric scattering, (e.g., atmospheric scattering, cloud), cloud), random or systematic malfunctionrandom or systematic malfunction of the remote of the remote sensing system (e.g., an sensing system (e.g., an uncalibrateduncalibrated detector detector creates striping), or creates striping), or improper preimproper pre--processingprocessing of the remote sensor of the remote sensor data prior to actual data analysis (e.g., inaccurate data prior to actual data analysis (e.g., inaccurate analoganalog--toto--digital conversion). digital conversion).

154 155

Cloud

155

160162

163164

MODISTrue143

Clouds in ETM+Clouds in ETM+

Striping Noise and RemovalStriping Noise and Removal

CPCACPCA

Combined Principle Combined Principle Component AnalysisComponent Analysis

Xie et al. 2004

Speckle Noise and Speckle Noise and RemovalRemoval

GG--MAPMAP

Blurred objectsBlurred objectsand boundaryand boundary

Gamma Maximum A Posteriori Filter

UnivariateUnivariate descriptive image statisticsdescriptive image statistics

The The modemode is the value that is the value that occurs most frequently in a occurs most frequently in a distribution and is usually the distribution and is usually the highest point on the curve highest point on the curve (histogram). It is common, (histogram). It is common, however, to encounter more however, to encounter more than one mode in a remote than one mode in a remote sensing dataset.sensing dataset.The The medianmedian is the value midway is the value midway in the frequency distribution. in the frequency distribution. OneOne--half of the area below the half of the area below the distribution curve is to the right distribution curve is to the right of the median, and oneof the median, and one--half is to half is to the leftthe leftThe The meanmean is the arithmetic is the arithmetic average and is defined as the average and is defined as the sum of all brightness value sum of all brightness value observations divided by the observations divided by the number of observations.number of observations.

n

BVn

iik

k

∑== 1µ

Cont’Cont’

( )

1var 1

2

−

−=

∑=

n

BVn

ikik

k

µMinMinMaxMaxVarianceVarianceStandard deviationStandard deviationCoefficient of Coefficient of variation (CV)variation (CV)SkewnessSkewnessKurtosisKurtosisMoment

kkks var== σ

k

kCVµσ

=Moment

Multivariate Image StatisticsMultivariate Image Statistics

Remote sensing research is often concerned Remote sensing research is often concerned with the measurement of how much radiant with the measurement of how much radiant flux is reflected or emitted from an object in flux is reflected or emitted from an object in more than one band. It is useful to compute more than one band. It is useful to compute multivariatemultivariate statistical measures such as statistical measures such as covariancecovariance and and correlationcorrelation among the several among the several bands to determine how the measurements bands to determine how the measurements covarycovary. Variance. Variance––covariance and correlation covariance and correlation matrices are used in remote sensing matrices are used in remote sensing principal principal components analysiscomponents analysis (PCA), (PCA), feature feature selectionselection, , classification and accuracy classification and accuracy assessmentassessment..

CovarianceCovarianceThe different remoteThe different remote--sensingsensing--derived spectral measurements derived spectral measurements for each pixel often change together in some predictable for each pixel often change together in some predictable fashion. If there is no relationship between the brightness fashion. If there is no relationship between the brightness value in one band and that of another for a given pixel, the value in one band and that of another for a given pixel, the values are mutually independent; that is, an increase or values are mutually independent; that is, an increase or decrease in one band’s brightness value is not accompanied decrease in one band’s brightness value is not accompanied by a predictable change in another band’s brightness value. by a predictable change in another band’s brightness value. Because spectral measurements of individual pixels may not Because spectral measurements of individual pixels may not be independent, some measure of their mutual interaction is be independent, some measure of their mutual interaction is needed. This measure, called the needed. This measure, called the covariancecovariance, is the joint , is the joint variation of two variables about their common mean. variation of two variables about their common mean.

( )n

BVBVBVBVSP

n

i

n

iilikn

iilikkl

∑ ∑∑ = =

=

−×= 1 1

1 1cov

−=

nSPkl

kl

CorrelationCorrelationTo estimate the degree of interrelation between variables in a manner not influenced by measurement units, the correlation coefficient, is commonly used. The correlation between two bands of remotely sensed data, rkl, is the ratio of their covariance (covkl) to the product of their standard deviations (sksl); thus:

To estimate the degree of interrelation between variables in a manner not influenced by measurement units, the correlation coefficient, is commonly used. The correlation between two bands of remotely sensed data, rkl, is the ratio of their covariance (covkl) to the product of their standard deviations (sksl); thus:

lk

klkl ss

r cov=

If we square the correlation coefficient (rkl), we obtain the sample coefficient of determination (r2), which expresses the proportion of the total variation in the values of “band l” that can be accounted for or explained by a linear relationship with the values of the random variable “band k.” Thus a correlation coefficient (rkl) of 0.70 results in an r2 value of 0.49, meaning that 49% of the total variation of the values of “band l” in the sample is accounted for by a linear relationship with values of “band k”.

If we square the correlation coefficient (rkl), we obtain the sample coefficient of determination (r2), which expresses the proportion of the total variation in the values of “band l” that can be accounted for or explained by a linear relationship with the values of the random variable “band k.” Thus a correlation coefficient (rkl) of 0.70 results in an r2 value of 0.49, meaning that 49% of the total variation of the values of “band l” in the sample is accounted for by a linear relationship with values of “band k”.

exampleexamplePixelPixel Band 1 Band 1 (green)(green)

Band 2 Band 2 (red)(red)

Band 3 Band 3 ((nini))

Band 4 Band 4 ((nini))

(1,1)(1,1) 130130 5757 180180 205205

(1,2)(1,2) 165165 3535 215215 255255

(1,3)(1,3) 100100 2525 135135 195195

(1,4)(1,4) 135135 5050 200200 220220

(1,5)(1,5) 145145 6565 205205 235235

( )( )

1354

540cov

5232675)860,31(

12

12

==

−=SP

Band 1Band 1 (Band 1 x Band (Band 1 x Band 2)2)

Band 2 Band 2

130130 7,4107,410 5757

165165 5,7755,775 3535

100100 2,5002,500 2525

135135 6,7506,750 5050

145145 9,4259,425 6565

675675 31,86031,860 232232

Band 1Band 1 Band 2 Band 2 Band 3Band 3 Band 4Band 4

Mean (Mean (µµkk)) 135135 46.4046.40 187187 222222

Variance (Variance (varvarkk)) 562.50562.50 264.80264.80 10071007 570570

((sskk)) 23.7123.71 16.2716.27 31.431.4 23.8723.87

((minminkk)) 100100 2525 135135 195195

((maxmaxkk)) 165165 6565 215215 255255

Range (Range (BVBVrr)) 6565 4040 8080 6060

Univariate statistics

Band 1Band 1 Band 2 Band 2 Band 3Band 3 Band 4Band 4

Band 1Band 1 562.25562.25 -- -- --

Band 2Band 2 135135 264.8264.800

-- --

Band 3Band 3 718.75718.75 275.25275.25 1007.51007.500

--

Band 4Band 4 537.50537.50 6464 663.75663.75 570570

covariance

Band Band 11

Band Band 2 2

Band 3Band 3 Band Band 44

Band 1Band 1 -- -- -- --

Band 2Band 2 0.350.35 -- -- --

Band 3Band 3 0.950.95 0.530.53 -- --

Band 4Band 4 0.940.94 0.160.16 0.870.87 --

Correlation coefficientCovariance

Types of radiometric correctionTypes of radiometric correction

Detector error or sensor error (internal Detector error or sensor error (internal error)error)Atmospheric error (external error)Atmospheric error (external error)Topographic error (external error)Topographic error (external error)

Atmospheric correctionAtmospheric correction

Solar irradiance

Reflectance from study area,

Various Paths of Satellite Received Radiance

Diffuse sky irradiance

Total radiance at the sensor

L L

L

Reflectance from neighboring area,

1

2

3

Remote sensor

detector

Atmosphere

5

4 1,3,5

θ

θ

E

L

90Þ

θ0T

θv T

0

0

v

p T

S

I

λ nr λ r

Ed

60 milesor100km

There are several ways to There are several ways to atmospherically correct atmospherically correct remotely sensed data. remotely sensed data. Some are relatively Some are relatively straightforward while straightforward while others are complex, others are complex, being founded on being founded on physical principles and physical principles and requiring a significant requiring a significant amount of information to amount of information to function properly. This function properly. This discussion will focus on discussion will focus on two major types of two major types of atmospheric correction:atmospheric correction:

Absolute atmospheric Absolute atmospheric correctioncorrection, and, andRelative atmospheric Relative atmospheric correctioncorrection..

Scattering, AbsorptionRefraction, Reflection

Absolute atmospheric correctionAbsolute atmospheric correction

Solar radiation is largely unaffected as it travels through the Solar radiation is largely unaffected as it travels through the vacuum of space. When it interacts with the Earth’s atmosphere, vacuum of space. When it interacts with the Earth’s atmosphere, however, it is selectively however, it is selectively scattered and absorbedscattered and absorbed. The sum of . The sum of these two forms of energy loss is called these two forms of energy loss is called atmospheric attenuationatmospheric attenuation..Atmospheric attenuation may 1) make it difficult to relate handAtmospheric attenuation may 1) make it difficult to relate hand--held held in situin situ spectroradiometerspectroradiometer measurements with remote measurements with remote measurements, 2) make it difficult to extend spectral signaturesmeasurements, 2) make it difficult to extend spectral signaturesthrough space and time, and (3) have an impact on classificationthrough space and time, and (3) have an impact on classificationaccuracy within a scene if atmospheric attenuation varies accuracy within a scene if atmospheric attenuation varies significantly throughout the image.significantly throughout the image.

The general goal of The general goal of absolute radiometric correctionabsolute radiometric correction is to turn is to turn the digital brightness values (or DN) recorded by a remote sensithe digital brightness values (or DN) recorded by a remote sensing ng system into system into scaled surface reflectancescaled surface reflectance values. Thesevalues. These values can values can then be compared or used in conjunction with scaled surface then be compared or used in conjunction with scaled surface reflectance values obtained anywhere else on the planet.reflectance values obtained anywhere else on the planet.

a) Image containing substantial haze prior to atmospheric correction. b) Image after atmospheric correction using ATCOR (Courtesy Leica Geosystems and DLR, the German Aerospace Centre).

a) Image containing substantial haze prior to atmospheric correction. b) Image after atmospheric correction using ATCOR (Courtesy Leica Geosystems and DLR, the German Aerospace Centre).

relative radiometric correctionrelative radiometric correction

When required data is not available for When required data is not available for absolute radiometric correction, we can absolute radiometric correction, we can do relative radiometric correctiondo relative radiometric correctionRelative radiometric correction may be Relative radiometric correction may be used toused to

SingleSingle--image normalization using histogram image normalization using histogram adjustmentadjustmentMultipleMultiple--data image normalization using data image normalization using regressionregression

SingleSingle--image normalization using image normalization using histogram adjustmenthistogram adjustment

The method is based on the fact that infrared The method is based on the fact that infrared data (>0.7 data (>0.7 µµm) is free of atmospheric m) is free of atmospheric scattering effects, whereas the visible region scattering effects, whereas the visible region (0.4(0.4--0.7 0.7 µµm) ism) is strongly influenced by them.strongly influenced by them.Use Use Dark SubtractDark Subtract to apply atmospheric to apply atmospheric scattering corrections to the image data. The scattering corrections to the image data. The digital number to subtract from each band digital number to subtract from each band can be either the can be either the band minimum, an averageband minimum, an averagebased upon a user defined region of interest, based upon a user defined region of interest, or or a specific valuea specific value

Dark Subtract using band minimumDark Subtract using band minimum

Topographic correctionTopographic correction

Topographic slope and aspect also introduce Topographic slope and aspect also introduce radiometric distortion (for example, areas in radiometric distortion (for example, areas in shadow)shadow)The goal of a slopeThe goal of a slope--aspect correction is to aspect correction is to remove topographically induced illumination remove topographically induced illumination variation so that two objects having the same variation so that two objects having the same reflectance properties show the same reflectance properties show the same brightness value (or DN) in the image despite brightness value (or DN) in the image despite their different orientation to the Sun’s positiontheir different orientation to the Sun’s positionBased on DEM, sunBased on DEM, sun--elevationelevation

Conceptions of geometric correctionConceptions of geometric correction

GeocodingGeocoding:: geographical referencinggeographical referencingRegistration:Registration: geographically or geographically or nongeographicallynongeographically (no coordination system)(no coordination system)

Image to Map (or Ground Image to Map (or Ground GeocorrectionGeocorrection))The correction of digital images to ground coordinates using groThe correction of digital images to ground coordinates using ground control und control points collected from maps (Topographic map, DLG) or ground GPS points collected from maps (Topographic map, DLG) or ground GPS points. points. Image to Image Image to Image GeocorrectionGeocorrection

Image to Image correction involves matching the coordinate systeImage to Image correction involves matching the coordinate systems or ms or column and row systems of two digital images with one image acticolumn and row systems of two digital images with one image acting as a ng as a reference image and the other as the image to be rectified.reference image and the other as the image to be rectified.

Spatial interpolation:Spatial interpolation: from input position to output position or coordinates. from input position to output position or coordinates. RST (rotation, scale, and transformation), Polynomial, TriangulaRST (rotation, scale, and transformation), Polynomial, TriangulationtionRoot Mean Square Error (RMS):Root Mean Square Error (RMS): The RMS is the error term used to The RMS is the error term used to determine the accuracy of the transformation from one system to determine the accuracy of the transformation from one system to another. It is another. It is the difference between the desired output coordinate for a GCP athe difference between the desired output coordinate for a GCP and the actual.nd the actual.

Intensity (or pixel value) interpolation (also called Intensity (or pixel value) interpolation (also called resamplingresampling):): The process of The process of extrapolating data values to a new grid, and is the step in rectextrapolating data values to a new grid, and is the step in rectifying an image that ifying an image that calculates pixel values for the rectified grid from the originalcalculates pixel values for the rectified grid from the original data grid. data grid.

Nearest neighbor, Bilinear, CubicNearest neighbor, Bilinear, Cubic

Image enhancementImage enhancement

image reduction, image reduction, image magnification, image magnification, transect extraction, transect extraction, contrast adjustments (linear and noncontrast adjustments (linear and non--linear),linear),band band ratioingratioing, , spatial filtering, spatial filtering, fourierfourier transformations, transformations, principle components analysis, principle components analysis, texture transformations, and texture transformations, and image sharpeningimage sharpening

Contrast Enhancement (stretch)Contrast Enhancement (stretch)

Materials or objects reflect or emit similar amounts of radiant Materials or objects reflect or emit similar amounts of radiant flux (so flux (so similar pixel value)similar pixel value)LowLow--contrast imagery with pixel range less than the designed contrast imagery with pixel range less than the designed radiometric rangeradiometric range

2020--100 for TM less than the designed 0100 for TM less than the designed 0--255255To improve the contrast:To improve the contrast:

Linear techniqueLinear techniqueMinimumMinimum--maximum contrast stretchmaximum contrast stretchPercentage linear contrast stretchPercentage linear contrast stretchStandard deviation contrast stretchStandard deviation contrast stretchPiecewise linear contrast stretch Piecewise linear contrast stretch

NonNon--linear technique linear technique Histogram equalizationHistogram equalization

Contrast enhancement is only intended to Contrast enhancement is only intended to improve the visual qualityimprove the visual qualityof a displayed image by increasing the range (spreading or of a displayed image by increasing the range (spreading or stretching) of data values to occupy the available image displaystretching) of data values to occupy the available image display range range (usually 0(usually 0--255). It does 255). It does not changenot change the pixel values, unless save it as the pixel values, unless save it as a new image. It is a new image. It is not goodnot good practice to use saved image for practice to use saved image for classification and change detection.classification and change detection.

MinimumMinimum--maximum contrast maximum contrast stretchstretch

kkk

kinout quantBVBV ⎟⎟

⎠

⎞⎜⎜⎝

⎛−−

=minmaxmin

where:- BVin is the original input brightness value - quantk is the range of the brightness values that can be

displayed on the CRT (e.g., 255),- mink is the minimum value in the image,- maxk is the maximum value in the image, and- BVout is the output brightness value

where:- BVin is the original input brightness value - quantk is the range of the brightness values that can be

displayed on the CRT (e.g., 255),- mink is the minimum value in the image,- maxk is the maximum value in the image, and- BVout is the output brightness value

Percentage linear and Percentage linear and standard deviation contrast standard deviation contrast

stretchstretchX percentage (say 5%) top or low values of the image X percentage (say 5%) top or low values of the image will be set to 0 or 255, rest of values will be linearly will be set to 0 or 255, rest of values will be linearly stretched to 0 to 255stretched to 0 to 255ENVI has a default of a 2% linear stretch applied to each ENVI has a default of a 2% linear stretch applied to each image band, meaning the bottom and top 2% of image image band, meaning the bottom and top 2% of image values are excluded by positioning the range bars at the values are excluded by positioning the range bars at the appropriate points. Low 2% and top 2% will be saturated appropriate points. Low 2% and top 2% will be saturated to 0 and 255, respectively. The values between the range to 0 and 255, respectively. The values between the range bars are then stretched linearly between 0 and 255 bars are then stretched linearly between 0 and 255 resulting in a new image. resulting in a new image. If the percentage coincides with a standard deviation If the percentage coincides with a standard deviation percentage, then it is called a standard deviation contrast percentage, then it is called a standard deviation contrast stretch. For a normal distribution, 68%, 95.4%, 99.73% stretch. For a normal distribution, 68%, 95.4%, 99.73% values lie in values lie in ±±11σσ, , ±±2 2 σσ, , ±±3 3 σσ. . So 16% linear contrast So 16% linear contrast stretch is the stretch is the ±±11σσ contrast stretch.contrast stretch.

original Saturating the waterStretching the land

Saturating the landStretching the water

Special linear contrast stretchOr Stretch on demand

Piecewise linear contrast stretchPiecewise linear contrast stretch

When the histogram of an image is not When the histogram of an image is not Gaussian (bimodal, Gaussian (bimodal, trimodaltrimodal, …), it is , …), it is possible to apply a piecewise linear contrast possible to apply a piecewise linear contrast stretch.stretch.But you better to know what each mode in But you better to know what each mode in the histogram represents in the real world.the histogram represents in the real world.

Stretch both Stretch both land and waterland and water

Principle Components Analysis (PCA)Principle Components Analysis (PCA)

There are large correlations among remote sensing bands. PCA wilThere are large correlations among remote sensing bands. PCA will result l result in another uncorrelated datasets: in another uncorrelated datasets: principal component images (PCs).principal component images (PCs). PC1 PC1 contains the largest variance contains the largest variance The first two or three components (PCs) contain over 90% of infoThe first two or three components (PCs) contain over 90% of information rmation from the original many bands. It is a greatfrom the original many bands. It is a great compresscompress operationoperationThe new principal component imagesThe new principal component images that may be more that may be more interpretableinterpretable than than the original data. the original data.

Purposes of image classificationPurposes of image classification

Land use and land cover (LULC)Land use and land cover (LULC)Vegetation typesVegetation typesGeologic terrainsGeologic terrainsMineral explorationMineral explorationAlteration mappingAlteration mapping…….…….

What is image classification or What is image classification or pattern recognitionpattern recognition

Is a process of classifying Is a process of classifying multispectralmultispectral ((hyperspectralhyperspectral) images into ) images into patterns of varying gray or assigned colorspatterns of varying gray or assigned colors that represent either that represent either

clustersclusters of statistically different sets of of statistically different sets of multibandmultiband data, some of which data, some of which can be correlated with separable classes/features/materials. Thcan be correlated with separable classes/features/materials. This is the is is the result of result of Unsupervised ClassificationUnsupervised Classification, or , or numerical discriminatorsnumerical discriminators composed of these sets of data that have been composed of these sets of data that have been grouped and specified by associating each with a particular grouped and specified by associating each with a particular classclass, etc. , etc. whose identity is known independently and which has representatiwhose identity is known independently and which has representative ve areas (training sites) within the image where that class is locaareas (training sites) within the image where that class is located. This is ted. This is the result of the result of Supervised ClassificationSupervised Classification. .

Spectral classesSpectral classes are those that are inherent in the remote sensor are those that are inherent in the remote sensor data and must be identified and then labeled by the analyst.data and must be identified and then labeled by the analyst.

Information classesInformation classes are those that human beings define. are those that human beings define.

unsupervised classification, The computer or algorithm automatically group pixels with similar spectral characteristics (means, standard deviations, covariance matrices, correlation matrices, etc.) into unique clusters according to some statistically determined criteria. The analyst then re-labels and combines the spectral clusters into information classes.

supervised classification. Identify known a priori through a combination of fieldwork, map analysis, and personal experience as training sites; the spectral characteristics of these sites are used to train the classification algorithm for eventual land-cover mapping of the remainder of the image. Every pixel both within and outside the training sites is then evaluated and assigned to the class of which it has the highest likelihood of being a member.

Hard vs. Fuzzy classificationHard vs. Fuzzy classification

SupervisedSupervised and and unsupervisedunsupervised classification classification algorithms typically use algorithms typically use hard classification hard classification logiclogicto produce a classification map that consists of to produce a classification map that consists of hard, discrete categories (e.g., forest, hard, discrete categories (e.g., forest, agriculture). agriculture).

Conversely, it is also possible to use Conversely, it is also possible to use fuzzy set fuzzy set classificationclassification logiclogic, which takes into account the , which takes into account the heterogeneous and imprecise nature (mix heterogeneous and imprecise nature (mix pixels) of the real world. Proportion of the m pixels) of the real world. Proportion of the m classes within a pixel (e.g., 10% bare soil, 10% classes within a pixel (e.g., 10% bare soil, 10% shrub, 80% forest). Fuzzy classification shrub, 80% forest). Fuzzy classification schemes are not currently standardized. schemes are not currently standardized.

PixelPixel--based vs. Objectbased vs. Object--oriented oriented classificationclassification

In the past, most digital image classification was based on In the past, most digital image classification was based on processing the entire scene pixel by pixel. This is commonly processing the entire scene pixel by pixel. This is commonly referred to as referred to as perper--pixel (pixelpixel (pixel--based) classificationbased) classification. .

ObjectObject--oriented classificationoriented classification techniques allow the techniques allow the analyst to decompose the scene into many relatively analyst to decompose the scene into many relatively homogenous image homogenous image objectsobjects (referred to as (referred to as patches or patches or segmentssegments) using a multi) using a multi--resolution image segmentation resolution image segmentation process. The various statistical characteristics of these process. The various statistical characteristics of these homogeneous image objects in the scene are then subjected homogeneous image objects in the scene are then subjected to traditional statistical or fuzzy logic classification. Objectto traditional statistical or fuzzy logic classification. Object--oriented classification based on image segmentation is often oriented classification based on image segmentation is often used for the analysis of highused for the analysis of high--spatialspatial--resolution imagery (e.g., resolution imagery (e.g., 11 ×× 1 m Space Imaging IKONOS and 0.611 m Space Imaging IKONOS and 0.61 ×× 0.61 m Digital 0.61 m Digital Globe Globe QuickBirdQuickBird).).

Unsupervised classificationUnsupervised classificationUses Uses statistical techniquesstatistical techniques to group nto group n--dimensional data into their natural dimensional data into their natural spectral clusters, and uses the spectral clusters, and uses the iterative proceduresiterative procedureslabel certain clusters as specific information classeslabel certain clusters as specific information classesKK--mean and ISODATAmean and ISODATA

For the first iteration arbitrary For the first iteration arbitrary starting valuesstarting values (i.e., the cluster properties) (i.e., the cluster properties) have to be selected. These have to be selected. These initial valuesinitial values can influence the outcome of the can influence the outcome of the classification.classification.In general, both methods assign first arbitrary initial cluster In general, both methods assign first arbitrary initial cluster values. The values. The second step classifies each pixel to the closest cluster. In thesecond step classifies each pixel to the closest cluster. In the third step the third step the new cluster mean vectors are calculated based on all the pixels new cluster mean vectors are calculated based on all the pixels in one in one cluster. The second and third steps are repeated until the "chancluster. The second and third steps are repeated until the "change" between ge" between the iteration is small. The "change" can be defined in several dthe iteration is small. The "change" can be defined in several different ways, ifferent ways, either by measuring the distances of the mean cluster vector haveither by measuring the distances of the mean cluster vector have changed e changed from one iteration to another or by the percentage of pixels thafrom one iteration to another or by the percentage of pixels that have t have changed between iterations. changed between iterations. The The ISODATA algorithm has some further refinementsISODATA algorithm has some further refinements by splitting and by splitting and merging of clusters. Clusters are merged if either the number ofmerging of clusters. Clusters are merged if either the number of members members (pixel) in a cluster is less than a certain threshold or if the (pixel) in a cluster is less than a certain threshold or if the centers of two centers of two clusters are closer than a certain threshold. Clusters are splitclusters are closer than a certain threshold. Clusters are split into two into two different clusters if the cluster standard deviation exceeds a pdifferent clusters if the cluster standard deviation exceeds a predefined value redefined value and the number of members (pixels) is twice the threshold for thand the number of members (pixels) is twice the threshold for the minimum e minimum number of members.number of members.

Supervised classification:Supervised classification:training sites selection training sites selection

Based on known a priori through a combination of fieldwork, Based on known a priori through a combination of fieldwork, map analysis, and personal experiencemap analysis, and personal experience

onon--screen selectionscreen selection of polygonal training data (ROI),of polygonal training data (ROI), and/or and/or

onon--screen seedingscreen seeding of training data (ENVI does not have of training data (ENVI does not have this, this, ErdasErdas Imagine does). Imagine does).

The The seedseed programprogram begins at a single begins at a single x, y x, y location and evaluates location and evaluates neighboring pixel values in all bands of interest. Using criterineighboring pixel values in all bands of interest. Using criteria a specified by the analyst, the seed algorithm expands outward likspecified by the analyst, the seed algorithm expands outward like e an amoeba as long as it finds pixels with spectral characteristian amoeba as long as it finds pixels with spectral characteristics cs similar to the original seed pixel. This is a very effective waysimilar to the original seed pixel. This is a very effective way of of collecting homogeneous training information. collecting homogeneous training information.

From From spectral libraryspectral library of field measurementsof field measurements

SelectingSelectingROIsROIs

Alfalfa

Cotton

Grass

Fallow

Supervised classification methodsSupervised classification methodsVarious supervised classification algorithms may be used to assiVarious supervised classification algorithms may be used to assign an unknown pixel to one gn an unknown pixel to one of of mm possible classes. The choice of a particular classifier or decipossible classes. The choice of a particular classifier or decision rule depends on the sion rule depends on the nature of the input data and the desired output. nature of the input data and the desired output. ParametricParametric classification algorithms classification algorithms assumes that the observed measurement vectors assumes that the observed measurement vectors XXcc obtained for each class in each spectral obtained for each class in each spectral band during the training phase of the supervised classification band during the training phase of the supervised classification are are GaussianGaussian; that is, they are ; that is, they are normally distributed. normally distributed. NonparametricNonparametric classification algorithms make no such assumption. classification algorithms make no such assumption.

Several widely adopted nonparametric classification algorithms iSeveral widely adopted nonparametric classification algorithms include:nclude:oneone--dimensional dimensional density slicingdensity slicingparallepipedparallepiped,,minimum distanceminimum distance, , nearestnearest--neighborneighbor, and , and neural network neural network andand expert system analysisexpert system analysis..

The most widely adopted parametric classification algorithms is The most widely adopted parametric classification algorithms is the:the:maximum likelihoodmaximum likelihood..

HyperspectralHyperspectral classification methodsclassification methodsBinary EncodingBinary EncodingSpectral Angle Spectral Angle MapperMapperMatched FilteringMatched FilteringSpectral Feature FittingSpectral Feature FittingLinear Spectral Linear Spectral UnmixingUnmixing

Supervisedclassificationmethod:

Spectral FeatureFitting

Source: http://popo.jpl.nasa.gov/html/data.html

Accuracy assessment of classificationAccuracy assessment of classification

Remote sensingRemote sensing--derived thematic information are derived thematic information are becoming increasingly important. Unfortunately, they becoming increasingly important. Unfortunately, they contain errors.contain errors.Errors come from 5 sources:Errors come from 5 sources:

Geometric error still thereGeometric error still thereNone of atmospheric correction is perfectNone of atmospheric correction is perfectClusters incorrectly labeled after unsupervised classificationClusters incorrectly labeled after unsupervised classificationTraining sites incorrectly labeled before supervised Training sites incorrectly labeled before supervised classificationclassificationNone of classification method is perfectNone of classification method is perfect

We should identify the sources of the error, minimize it, We should identify the sources of the error, minimize it, do accuracy assessment, create metadata before being do accuracy assessment, create metadata before being used in scientific investigations and policy decisions. used in scientific investigations and policy decisions. We usually need GIS layers to assist our classification.We usually need GIS layers to assist our classification.

training vs. ground referencetraining vs. ground reference

Several ways to do error evaluationSeveral ways to do error evaluationBased on training pixels (areas)Based on training pixels (areas)

The problem is that the locations of training sites are usually The problem is that the locations of training sites are usually not not random. They are biased by analyst’s a priori knowledge of random. They are biased by analyst’s a priori knowledge of where certain LULC types exist in the scene. where certain LULC types exist in the scene. This will results in higher classification accuracies than the oThis will results in higher classification accuracies than the one ne belowbelow

Based on ground reference pixelsBased on ground reference pixelsThese sites are not used to train the classification algorithm aThese sites are not used to train the classification algorithm and nd therefore represent unbiased reference informationtherefore represent unbiased reference informationIt is possible to collect some ground sites prior to the It is possible to collect some ground sites prior to the classification, perhaps at the same time as the training dataclassification, perhaps at the same time as the training dataBut majority of test reference is often collected after But majority of test reference is often collected after classification.classification.

Landscape often change rapidly. Therefore, it is best to Landscape often change rapidly. Therefore, it is best to collect both the training and ground reference as close collect both the training and ground reference as close to the data of remote sensing data acquisition as to the data of remote sensing data acquisition as possible. (for example, agriculture crops change fast)possible. (for example, agriculture crops change fast)

Error (Confusion) MatrixError (Confusion) MatrixProducerProducer (analyst) accuracy(analyst) accuracy is a measure indicating the probability that is a measure indicating the probability that the classifier has labeled an image pixel into Class A given thathe classifier has labeled an image pixel into Class A given that the t the ground truth is Class A. it is the probability of a reference piground truth is Class A. it is the probability of a reference pixel being xel being correctly classified.correctly classified.Omission errorOmission error represent pixels that belong to the ground truth class but represent pixels that belong to the ground truth class but that the classification technique has failed to classify them inthat the classification technique has failed to classify them into the to the proper class.proper class.UserUser accuracyaccuracy is a measure indicating the probability that a pixel is Class is a measure indicating the probability that a pixel is Class A given that the classifier has labeled the pixel into Class A. A given that the classifier has labeled the pixel into Class A. it is the it is the probability that a pixel classified on the map actually represenprobability that a pixel classified on the map actually represents that ts that category on the ground.category on the ground.Commission errorCommission error represent pixels that belong to another class but are represent pixels that belong to another class but are labeled as belonging to the class.labeled as belonging to the class.Overall accuracyOverall accuracy is total classification accuracy.is total classification accuracy.Kappa coefficientKappa coefficient (K(Khathat) is a discrete multivariate technique of use in ) is a discrete multivariate technique of use in accuracy assessment. Kaccuracy assessment. Khathat>80% represent strong agreement and good >80% represent strong agreement and good accuracy. 40%accuracy. 40%--80% is middle, <40% is poor.80% is middle, <40% is poor.

Example: they took 407 samples (pixels) based on the stratified random sampling after classification. First made 5 files (each contain one class), using a random number generator to get points.

PostPost--classification and GISclassification and GIS

salt-and-pepper

typestypes

Majority/Minority AnalysisMajority/Minority AnalysisClump ClassesClump ClassesMorphology FiltersMorphology FiltersSieve ClassesSieve ClassesCombine ClassesCombine ClassesClassification to vector (GIS)Classification to vector (GIS)

Change detectionChange detectionChange detect involves the use of multiChange detect involves the use of multi--temporal datasets to temporal datasets to discriminate areas of land cover change between dates of imagingdiscriminate areas of land cover change between dates of imaging..Ideally, it requires Ideally, it requires

Same or similar sensor, resolution, viewing geometry, spectral bSame or similar sensor, resolution, viewing geometry, spectral bands, ands, radiomatricradiomatric resolution, acquisition time of data, and anniversary datesresolution, acquisition time of data, and anniversary datesAccurate spatial registration (less than 0.5 pixel error)Accurate spatial registration (less than 0.5 pixel error)

MethodsMethodsIndependently classified and registered, then compare themIndependently classified and registered, then compare themClassification of combined multiClassification of combined multi--temporal datasets, temporal datasets, Principal components analysis of combined multiPrincipal components analysis of combined multi--temporal datasetstemporal datasetsImage differencing (subtracting), (needs to find change/no changImage differencing (subtracting), (needs to find change/no change threshold, e threshold, change area will be in the tails of the histogram distribution)change area will be in the tails of the histogram distribution)Image Image ratioingratioing (dividing), (needs to find change/no change threshold, (dividing), (needs to find change/no change threshold, change area will be in the tails of the histogram distribution)change area will be in the tails of the histogram distribution)Change vector analysisChange vector analysisDelta transformationDelta transformation

Example: stages of developmentExample: stages of development

Sun City –Hilton HeadSun City –

Hilton Head

19941994

19961996

1974

1,040 urbanhectares

1994

3,263 urbanhectares

315% increase

19741974

1,040 urban1,040 urbanhectareshectares

19941994

3,263 urbanhectares

315% increase