Intro of digital image Intro of digital image processingprocessing

Lecture 5aLecture 5a

Remote Sensing Raster (Matrix) Data FormatRemote Sensing Raster (Matrix) Data Format Remote Sensing Raster (Matrix) Data FormatRemote Sensing Raster (Matrix) Data Format

Digital number of column 5, row 4 at band 2 is expressed as BV5,4,2 = 105.

Image file formatsImage file formats

BSQ (Band Sequential Format): BSQ (Band Sequential Format): each line of the data followed immediately by the next line in the each line of the data followed immediately by the next line in the

same spectral band. This format is optimal for spatial (X, Y) access of same spectral band. This format is optimal for spatial (X, Y) access of any part of a single spectral band. Good for multispectral imagesany part of a single spectral band. Good for multispectral images

BIP (Band Interleaved by Pixel Format):BIP (Band Interleaved by Pixel Format): the first pixel for all bands in sequential order, followed by the the first pixel for all bands in sequential order, followed by the

second pixel for all bands, followed by the third pixel for all bands, second pixel for all bands, followed by the third pixel for all bands, etc., interleaved up to the number of pixels. This format provides etc., interleaved up to the number of pixels. This format provides optimum performance for spectral (Z) access of the image data. optimum performance for spectral (Z) access of the image data. Good for hyperspectral imagesGood for hyperspectral images

BIL (Band Interleaved by Line Format):BIL (Band Interleaved by Line Format): the first line of the first band followed by the first line of the second the first line of the first band followed by the first line of the second

band, followed by the first line of the third band, interleaved up to band, followed by the first line of the third band, interleaved up to the number of bands. Subsequent lines for each band are interleaved the number of bands. Subsequent lines for each band are interleaved in similar fashion. This format provides a compromise in in similar fashion. This format provides a compromise in performance between spatial and spectral processing and is the performance between spatial and spectral processing and is the recommended file format for most ENVI processing tasks. Good for recommended file format for most ENVI processing tasks. Good for images with 20-60 bandsimages with 20-60 bands

120 150 100 120 103

176 166 155 85 150

85 80 70 77 135

103 90 70 120 133

20 50 50 90 90

76 66 55 45 120

80 80 60 70 150

100 93 97 101 105

210 250 250 190 245

156 166 155 415 220

180 180 160 170 200

200 0 123 222 215

Band 2 Band 3 Band 4

1,1,22,1,

2 3,1,2 4,1,2 5,1,2

1,2,22,2,

2 3,2,2 4,2,2 5,2,2

1,3,22,3,

2 3,3,2 4,3,2 5,3,2

1,4,22,4,

2 3,4,2 4,4,2 5,4,2

Matrix notation for band 2

10 15 17 20 21

15 16 18 21 23

17 18 20 22 22

18 20 22 24 25

20 50 50 90 90

76 66 55 45 120

80 80 60 70 150

100 93 97 101 105

120 150 100 120 103

176 166 155 85 150

85 80 70 77 135

103 90 70 120 133

210 250 250 190 245

156 166 155 415 220

180 180 160 170 200

200 0 123 222 215

BIL

10 15 17 20 21

20 50 50 90 90

120 150 100 120 103

210 250 250 190 245

15 16 18 21 23

76 66 55 45 120

176 166 155 85 150

156 166 155 415 220

17 18 20 22 22

80 80 60 70 150

85 80 70 77 135

180 180 160 170 200

18 20 22 24 25

100 93 97 101 105

103 90 70 120 133

200 0 123 222 215

BSQ

10 20 120 210 15

15 76 176 156 16

17 80 85 180 18

18 100 103 200 20

50 150 250 17 50

66 166 166 18 55

80 80 180 20 60

93 90 0 22 97

100 250 20 90 120

155 155 21 45 85

70 160 22 70 77

70 123 24 101 120

190 21 90 103 245

415 23 120 150 220

170 22 150 135 200

222 25 105 133 215

BIP

Band sequential (BSQ) format stores Band sequential (BSQ) format stores information for the image one band at information for the image one band at a time. In other words, data for all a time. In other words, data for all pixels for band 1 is stored first, then pixels for band 1 is stored first, then data for all pixels for band 2, and so data for all pixels for band 2, and so on.on.

Value=image(c, r, b)

Band interleaved by pixel (BIP) data is Band interleaved by pixel (BIP) data is similar to BIL data, except that the similar to BIL data, except that the data for each pixel is written band by data for each pixel is written band by band. For example, with the same band. For example, with the same three-band image, the data for bands three-band image, the data for bands 1, 2 and 3 are written for the first pixel 1, 2 and 3 are written for the first pixel in column 1; the data for bands 1, 2 in column 1; the data for bands 1, 2 and 3 are written for the first pixel in and 3 are written for the first pixel in column 2; and so on.column 2; and so on.

Value=image(b, c, r)

Band interleaved by line (BIL) data Band interleaved by line (BIL) data stores pixel information band by band stores pixel information band by band for each line, or row, of the image. For for each line, or row, of the image. For example, given a three-band image, example, given a three-band image, all three bands of data are written for all three bands of data are written for row 1, all three bands of data are row 1, all three bands of data are written for row 2, and so on, until the written for row 2, and so on, until the total number of rows in the image is total number of rows in the image is reached. reached.

Value=image(c, b, r)

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 image

Computerized routines for information Computerized routines for information extraction (eg, pattern recognition, extraction (eg, pattern recognition, classification) from remotely sensed classification) from remotely sensed images to obtain categories of images to obtain categories of information about specific features.information about specific features.

Many more

Image Processing Includes Image Processing Includes

Image quality and statistical evaluationImage quality and statistical evaluation Radiometric correctionRadiometric correction Geometric correctionGeometric correction Image enhancement and sharpeningImage enhancement and sharpening Image classificationImage classification

Pixel basedPixel based Object-oriented basedObject-oriented based

Accuracy assessment of classificationAccuracy assessment of classification Post-classification and GISPost-classification and GIS Change detectionChange detection

GEO5083: Remote Sensing Image Processing and Analysis, spring 2012

Image QualityImage Quality

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

scattering, cloud), scattering, cloud), random or systematic malfunctionrandom or systematic malfunction of the of the

remote sensing system (e.g., an uncalibrated remote sensing system (e.g., an uncalibrated detector creates striping), or detector creates striping), or

improper pre-processingimproper pre-processing of the remote sensor of the remote sensor data prior to actual data analysis (e.g., data prior to actual data analysis (e.g., inaccurate analog-to-digital conversion). inaccurate analog-to-digital conversion).

155

154 155

160162

163164

MODISTrue143

Cloud

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 Speckle Noise and Removaland Removal

G-MAPG-MAP

Blurred objectsBlurred objectsand boundaryand boundary

Gamma Maximum A Posteriori Filter

Univariate descriptive image Univariate descriptive image statisticsstatistics

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 distribution and is usually the highest point on the the highest point on the curve (histogram). It is curve (histogram). It is common, however, to common, however, to encounter more than one encounter more than one mode in a remote sensing mode in a remote sensing dataset.dataset.

The The medianmedian is the value is the value midway in the frequency midway in the frequency distribution. One-half of the distribution. One-half of the area below the distribution area below the distribution curve is to the right of the curve is to the right of the median, and one-half is to median, and one-half is to the leftthe left

The The meanmean is the arithmetic is the arithmetic average and is defined as average and is defined as the sum of all brightness the sum of all brightness value observations divided value observations divided by the number of by the number of observations.observations.

n

BVn

iik

k

1

n

BVn

iik

k

1

Cont’Cont’

MinMin MaxMax VarianceVariance Standard deviationStandard deviation Coefficient of Coefficient of

variation (CV)variation (CV) SkewnessSkewness KurtosisKurtosis MomentMoment

1var 1

2

n

BVn

ikik

k

kkks var

k

kCV

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 covary. Variance–covariance and correlation covary. Variance–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..

CovarianceCovariance The different remote-sensing-derived spectral The different remote-sensing-derived spectral

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

n

BVBVBVBVSP

n

i

n

iilikn

iilikkl

1 1

1

n

BVBVBVBVSP

n

i

n

iilikn

iilikkl

1 1

1 1cov

n

SPklkl 1

cov

n

SPklkl

CorrelationCorrelation

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:

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

rcov

lk

klkl ss

rcov

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”.

exampleexample

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

1354

540cov

5

232675)860,31(

12

12

SP

1354

540cov

5

232675)860,31(

12

12

SP

PixelPixel Band 1 Band 1 (green)(green)

Band 2 Band 2 (red)(red)

Band 3 Band 3 (ni)(ni)

Band 4 Band 4 (ni)(ni)

(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

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

Mean (Mean (kk)) 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

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

Band 1Band 1 562.2562.255

-- -- --

Band 2Band 2 135135 264.8264.800

-- --

Band 3Band 3 718.75718.75 275.2275.255

1007.1007.5050

--

Band 4Band 4 537.50537.50 6464 663.75663.75 570570

Univariate statistics

covariance

Band Band 11

Band Band 2 2

Band Band 33

Band Band 44

Band Band 11

-- -- -- --

Band Band 22

0.350.35 -- -- --

Band Band 33

0.950.95 0.530.53 -- --

Band Band 44

0.940.94 0.160.16 0.870.87 --Covariance Correlation coefficient

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

There are several ways There are several ways to atmospherically to atmospherically correct remotely correct remotely sensed data. Some are sensed data. Some are relatively 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 amount of information to function properly. to function properly. This discussion will This discussion will focus on two major focus on two major types of atmospheric types of atmospheric correction:correction:

Absolute atmospheric Absolute atmospheric correctioncorrection, and, and

Relative atmospheric Relative atmospheric correctioncorrection..

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

E

L

90Þ

0T

v T

0

0

v

p T

S

I

nr r

Ed

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

E

L

90Þ

0T

v T

0

0

v

p T

S

I

nr r

Ed

60 milesor100km

Scattering, AbsorptionRefraction, Reflection

Absolute atmospheric Absolute atmospheric correctioncorrection

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 vacuum of space. When it interacts with the Earth’s atmosphere, however, it is selectively atmosphere, however, it is selectively scattered and scattered and absorbedabsorbed. The sum of these two forms of energy loss is called . The sum of these two forms of energy loss is called atmospheric attenuationatmospheric attenuation.. Atmospheric attenuation may 1) make Atmospheric attenuation may 1) make it difficult to relate hand-held it difficult to relate hand-held in situin situ spectroradiometer spectroradiometer measurements with remote measurements, 2) make it difficult measurements with remote measurements, 2) make it difficult to extend spectral signatures through space and time, and (3) to extend spectral signatures through space and time, and (3) have an impact on classification accuracy within a scene if have an impact on classification accuracy within a scene if atmospheric attenuation varies significantly throughout the atmospheric attenuation varies significantly throughout the image.image.

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

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 correction

Relative radiometric correction may be Relative radiometric correction may be used toused to Single-image normalization using histogram Single-image normalization using histogram

adjustmentadjustment Multiple-data image normalization using Multiple-data image normalization using

regressionregression

Single-image normalization Single-image normalization using histogram adjustmentusing histogram 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 m) is free of atmospheric m) is free of atmospheric scattering effects, whereas the visible region scattering effects, whereas the visible region (0.4-0.7 (0.4-0.7 m) is strongly influenced by them.m) is 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 average based 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 Dark Subtract using band minimumminimum

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 slope-aspect correction is to The goal of a slope-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 position

Based on DEM, sun-elevationBased on DEM, sun-elevation

Conceptions of geometric Conceptions of geometric correctioncorrection

Geocoding:Geocoding: geographical referencing geographical referencing Registration:Registration: geographically or nongeographically (no coordination system) geographically or nongeographically (no coordination system)

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

Image to Image GeocorrectionImage to Image GeocorrectionImage to Image correction involves matching the coordinate systems or Image to Image correction involves matching the coordinate systems or column and row systems of two digital images with one image acting as column and row systems of two digital images with one image acting as a reference image and the other as the image to be rectified.a 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, TriangulationRST (rotation, scale, and transformation), Polynomial, Triangulation Root 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 the difference between the desired output coordinate for a another. It is the difference between the desired output coordinate for a GCP and the actual.GCP and the actual.

Intensity (or pixel value) interpolation (also called resampling):Intensity (or pixel value) interpolation (also called resampling): The process The process of extrapolating data values to a new grid, and is the step in rectifying an of extrapolating data values to a new grid, and is the step in rectifying an image that calculates pixel values for the rectified grid from the original data image that calculates pixel values for the rectified grid from the original data grid. 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 non-linear),contrast adjustments (linear and non-linear), band ratioing, band ratioing, spatial filtering, spatial filtering, fourier transformations, fourier transformations, principle components analysis, principle components analysis, texture transformations, and texture transformations, and image sharpeningimage sharpening

Purposes of image Purposes of image classificationclassification

Land use and land cover (LULC)Land use and land cover (LULC)

Vegetation typesVegetation types

Geologic terrainsGeologic terrains

Mineral explorationMineral exploration

Alteration mappingAlteration mapping

…………..

What is image What is image classification or classification or

pattern recognitionpattern recognition

Is a process of classifying multispectral (hyperspectral) images Is a process of classifying multispectral (hyperspectral) images into into patterns of varying gray or assigned colorspatterns of varying gray or assigned colors that represent that represent either either clustersclusters of statistically different sets of multiband data, some of of statistically different sets of multiband data, some of

which can be correlated with separable classes/features/materials. which can be correlated with separable classes/features/materials. This is the result of This is the result of Unsupervised ClassificationUnsupervised Classification, or , or

numerical discriminatorsnumerical discriminators composed of these sets of data that have composed of these sets of data that have been grouped and specified by associating each with a particular been grouped and specified by associating each with a particular classclass, etc. whose identity is known independently and which has , etc. whose identity is known independently and which has representative areas (training sites) within the image where that representative areas (training sites) within the image where that class is located. This is the result of class is located. This is the result of Supervised ClassificationSupervised Classification. .

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

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

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.

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.

Hard vs. Fuzzy classificationHard vs. Fuzzy classification

SupervisedSupervised and and unsupervisedunsupervised classification classification algorithms typically use algorithms typically use hard classification hard classification logiclogic to 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 logic logic, 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 schemes shrub, 80% forest). Fuzzy classification schemes are not currently standardized. are not currently standardized.

Pixel-based vs. Object-Pixel-based vs. Object-oriented classificationoriented classification

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 per-pixel (pixel-based) classificationper-pixel (pixel-based) classification. .

Object-oriented classificationObject-oriented 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-resolution image segmentation ) using a multi-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. Object-to 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 high-spatial-resolution imagery (e.g., used for the analysis of high-spatial-resolution imagery (e.g., 1 1  1 m Space Imaging IKONOS and 0.61  1 m Space Imaging IKONOS and 0.61  0.61 m Digital  0.61 m Digital Globe QuickBird).Globe QuickBird).

Unsupervised classificationUnsupervised classification Uses Uses statistical techniquesstatistical techniques to group n-dimensional data into their to group n-dimensional data into their

natural spectral clusters, and uses the natural spectral clusters, and uses the iterative proceduresiterative procedures label certain clusters as specific information classeslabel certain clusters as specific information classes K-mean and ISODATAK-mean and ISODATA

For the first iteration arbitrary For the first iteration arbitrary starting valuesstarting values (i.e., the cluster (i.e., the cluster properties) have to be selected. These properties) have to be selected. These initial valuesinitial values can influence the can influence the outcome of the classification.outcome of the classification.

In general, both methods assign first arbitrary initial cluster values. In general, both methods assign first arbitrary initial cluster values. The second step classifies each pixel to the closest cluster. In the The second step classifies each pixel to the closest cluster. In the third step the new cluster mean vectors are calculated based on all third step the new cluster mean vectors are calculated based on all the pixels in one cluster. The second and third steps are repeated the pixels in one cluster. The second and third steps are repeated until the "change" between the iteration is small. The "change" can until the "change" between the iteration is small. The "change" can be defined in several different ways, either by measuring the be defined in several different ways, either by measuring the distances of the mean cluster vector have changed from one iteration distances of the mean cluster vector have changed from one iteration to another or by the percentage of pixels that have changed between to another or by the percentage of pixels that have changed between iterations. 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 of merging of clusters. Clusters are merged if either the number of members (pixel) in a cluster is less than a certain threshold or if the members (pixel) in a cluster is less than a certain threshold or if the centers of two clusters are closer than a certain threshold. Clusters centers of two clusters are closer than a certain threshold. Clusters are split into two different clusters if the cluster standard deviation are split into two different clusters if the cluster standard deviation exceeds a predefined value and the number of members (pixels) is exceeds a predefined value and the number of members (pixels) is twice the threshold for the minimum number of members.twice the threshold for the minimum 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

on-screen selectionon-screen selection of polygonal training data (ROI), of polygonal training data (ROI), and/or and/or

on-screen seedingon-screen seeding of training data (ENVI does not have of training data (ENVI does not have this, Erdas Imagine does). this, Erdas 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 criteria neighboring pixel values in all bands of interest. Using criteria specified by the analyst, the seed algorithm expands outward specified by the analyst, the seed algorithm expands outward like an amoeba as long as it finds pixels with spectral like an amoeba as long as it finds pixels with spectral characteristics similar to the original seed pixel. This is a very characteristics similar to the original seed pixel. This is a very effective way of collecting homogeneous training information. effective way of collecting homogeneous training information.

From From spectral libraryspectral library of field measurements of field measurements

SelectingSelectingROIsROIs

Alfalfa

Cotton

Grass

Fallow

Supervised classification Supervised classification methodsmethods

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

Several widely adopted nonparametric classification algorithms include:Several widely adopted nonparametric classification algorithms include: one-dimensional one-dimensional density slicingdensity slicing parallepipedparallepiped,, minimum distanceminimum distance, , nearest-neighbornearest-neighbor, and , and neural network neural network andand expert system analysis expert system analysis..

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

Hyperspectral classification methodsHyperspectral classification methods Binary EncodingBinary Encoding Spectral Angle MapperSpectral Angle Mapper Matched FilteringMatched Filtering Spectral Feature FittingSpectral Feature Fitting Linear Spectral UnmixingLinear Spectral Unmixing

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

Supervisedclassificationmethod:

Spectral FeatureFitting

Accuracy assessment of Accuracy assessment of classificationclassification

Remote sensing-derived thematic information are Remote sensing-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 there None of atmospheric correction is perfectNone of atmospheric correction is perfect Clusters incorrectly labeled after unsupervised classificationClusters incorrectly labeled after unsupervised classification Training sites incorrectly labeled before supervised classificationTraining sites incorrectly labeled before supervised classification None 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.

Post-classification and GISPost-classification and GIS

salt-and-pepper

typestypes

Majority/Minority AnalysisMajority/Minority Analysis Clump ClassesClump Classes Morphology FiltersMorphology Filters Sieve ClassesSieve Classes Combine ClassesCombine Classes Classification to vector (GIS)Classification to vector (GIS)

Change detectionChange detection Change detect involves the use of multi-temporal datasets to Change detect involves the use of multi-temporal datasets to

discriminate areas of land cover change between dates of discriminate areas of land cover change between dates of imaging.imaging.

Ideally, it requires Ideally, it requires Same or similar sensor, resolution, viewing geometry, spectral bands, Same or similar sensor, resolution, viewing geometry, spectral bands,

radiomatric resolution, acquisition time of data, and anniversary datesradiomatric resolution, acquisition time of data, and anniversary dates Accurate spatial registration (less than 0.5 pixel error)Accurate spatial registration (less than 0.5 pixel error)

MethodsMethods Independently classified and registered, then compare themIndependently classified and registered, then compare them Classification of combined multi-temporal datasets, Classification of combined multi-temporal datasets, Principal components analysis of combined multi-temporal datasetsPrincipal components analysis of combined multi-temporal datasets Image differencing (subtracting), (needs to find change/no change Image differencing (subtracting), (needs to find change/no change

threshold, change area will be in the tails of the histogram threshold, change area will be in the tails of the histogram distribution)distribution)

Image ratioing (dividing), (needs to find change/no change threshold, Image ratioing (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 analysis Delta transformationDelta transformation

Example: stages of Example: stages of developmentdevelopment

19941994

19961996

Sun City – Hilton Head

Sun City – Hilton Head

19741974

1,040 urban1,040 urbanhectareshectares

19941994

3,263 urbanhectares

315% increase

19741974

1,040 urban1,040 urbanhectareshectares

19941994

3,263 urbanhectares

315% increase