Remote SensingRemote Sensing

Image EnhancementImage Enhancement

Image EnhancementImage Enhancement

► Increases distinction between Increases distinction between features in a scenefeatures in a scene

► Single image manipulationSingle image manipulation► Multi-image manipulationMulti-image manipulation

Single ImageSingle Image

► Contrast manipulationContrast manipulation► Spatial feature manipulationSpatial feature manipulation

1. Contrast Manipulation1. Contrast Manipulation

► Gray-level thresholdGray-level threshold► Level slicingLevel slicing► Contrast stretchingContrast stretching► Histogram-equalized stretchingHistogram-equalized stretching

Contrast Manipulation ..Contrast Manipulation ..

► Gray-level thresholdGray-level threshold

segmenting an image into two classes segmenting an image into two classes - binary mask- binary mask

► Level slicingLevel slicing

dividing the histogram of DNs into dividing the histogram of DNs into several slicesseveral slices

Color-coded temperature maps derived from NIMBUS

http://rst.gsfc.nasa.gov/Sect14/Sect14_4.html

Contrast Manipulation ..Contrast Manipulation ..

► Contrast stretchingContrast stretching

Expanding a narrow range of DNs to a Expanding a narrow range of DNs to a full rangefull range DN - Min DN - Min

Linear stretch: DN = (-------------) Linear stretch: DN = (-------------) *255*255

Max - MinMax - Min► Advantage: simple computation Advantage: simple computation Disadvantage: rare and frequent values Disadvantage: rare and frequent values have the same amount of levelshave the same amount of levels

StretchingStretching

Contrast Manipulation ..Contrast Manipulation ..

► Histogram-equalized stretchingHistogram-equalized stretching► Stretch based on frequency of Stretch based on frequency of occurrenceoccurrence

► Frequently occurred DNs have more Frequently occurred DNs have more display levelsdisplay levels

► Special stretchSpecial stretch

2. Spatial Feature 2. Spatial Feature ManipulationManipulation

► Spatial filteringSpatial filtering► Edge enhancementEdge enhancement► ConvolutionConvolution► Directional first differencingDirectional first differencing

Spatial FilteringSpatial Filtering

► Low pass filters emphasize low Low pass filters emphasize low frequency featuresfrequency features

► Compute the average values of Compute the average values of moving windowsmoving windows

Low Pass FilterLow Pass Filter

3 4 5 0 1

6 8 3 1 5

3 4 0 2 1

3 8 0 5 1

4 3 2

4 4 2

MeanMoving windows

Spatial Filtering ..Spatial Filtering ..

► High pass filters emphasize local High pass filters emphasize local detailsdetails

► It subtracts the low-pass filter It subtracts the low-pass filter from the original imagefrom the original image

Edge EnhancementEdge Enhancement

► Add back the high frequency image Add back the high frequency image component to the original image component to the original image

► Preserve both the original and the Preserve both the original and the high frequency featureshigh frequency features

ConvolutionConvolution

► A moving kernel with a weighting A moving kernel with a weighting factor for each pixelfactor for each pixel

ConvolutionConvolution

Directional DifferencingDirectional Differencing

► Displaying the differences in gray Displaying the differences in gray levels of adjacent pixelslevels of adjacent pixels

► The direction can be horizontal, The direction can be horizontal, vertical, or diagonalvertical, or diagonal

► It is necessary to add a constant to It is necessary to add a constant to the difference for display purposesthe difference for display purposes

► Add back the directional difference Add back the directional difference to the original imageto the original image

► Contrast stretching is needed for Contrast stretching is needed for all feature manipulationsall feature manipulations

ConvolutionConvolution

3. Multi-image Manipulation3. Multi-image Manipulation

► Spectral ratioingSpectral ratioing► Principle component transformation Principle component transformation ► Kauth-Thomas tasseled capKauth-Thomas tasseled cap► Intensity-Hue-Saturation Intensity-Hue-Saturation transformation (IHS)transformation (IHS)

3.1 Spectral Ratioing3.1 Spectral Ratioing

► A ratio of two bands (with great A ratio of two bands (with great difference in reflectance)difference in reflectance)

► Useful to eliminate effects of Useful to eliminate effects of illumination differencesillumination differences

► Select bands with distinct spectral Select bands with distinct spectral responsesresponses

► Necessary to stretch the resultant Necessary to stretch the resultant values to a full range of DN values values to a full range of DN values after ratioingafter ratioing

Band Ratioing ..Band Ratioing ..

Band Ratioing ..Band Ratioing ..

► Based on the observation that the DNs for a Based on the observation that the DNs for a same feature are lower in the shadow, and same feature are lower in the shadow, and the DNs are reduced in a similar proportion the DNs are reduced in a similar proportion between featuresbetween features

4848 3131 1111 1818

4848 3131 1111 1818

4848 3131 1111 1818

4848 3131 1111 1818

.96.96 .69.69 .69.69 .95.95

.96.96 .69.69 .69.69 .95.95

.96.96 .69.69 .69.69 .95.95

.96.96 .69.69 .69.69 .95.95

=÷

5050 4545 1616 1919

5050 4545 1616 1919

5050 4545 1616 1919

5050 4545 1616 1919

Band A

Band B

Ratio Band

÷ =

Hybrid Color Ratio Hybrid Color Ratio CompositeComposite

► Problem: different features but of Problem: different features but of similar ratio may appear identical similar ratio may appear identical

► Solution: when display, combine two Solution: when display, combine two ratio bands + one original band to ratio bands + one original band to restore the absolute DN valuesrestore the absolute DN values

3.2 Principle Component 3.2 Principle Component TransformationTransformation

► To reduce redundancy in multi-spectral dataTo reduce redundancy in multi-spectral data► The transformThe transform

DNDNII = a = a1111DNDNAA + a + a1212DNDNB B + a+ a1313DNDNC C + a+ a1414DNDNDD

DNDNIIII = a = a2121DNDNAA + a + a2222DNDNB B + a+ a2323DNDNC C + a+ a2424DNDNDD

DNDNIIIIII = a = a3131DNDNAA + a + a3232DNDNB B + a+ a3333DNDNC C + a+ a3434DNDNDD

DNDNIVIV = a = a4141DNDNAA + a + a4242DNDNB B + a+ a4343DNDNC C + a+ a4444DNDNDD

DNDNI, I, - DN- DNIV, IV, - DNs in new component images- DNs in new component images

DNDNA, A, -DN-DNDD - DNs in the original images - DNs in the original images

aa11, 11, aa12,,,,12,,,,aa44 44 - coefficients for the - coefficients for the transformationtransformation

PCAPCA

PC Transformation ..PC Transformation ..

► After the axes rotation, the After the axes rotation, the original n bands images are converted original n bands images are converted into n principle components imagesinto n principle components images

► The first component (PC1) image The first component (PC1) image contains the largest percentage of contains the largest percentage of the total scene variance (90%+)the total scene variance (90%+)

► The second component (PC2) contains The second component (PC2) contains the largest of the remaining the largest of the remaining variancevariance

PC Transformation ..PC Transformation ..

► Percentage of variance explained by Percentage of variance explained by each componenteach component

► %: 84.68 10.99 3.15 0.56 0.33 %: 84.68 10.99 3.15 0.56 0.33 0.18 0.100.18 0.10

► Cul: 84.68 95.67 98.82 99.38 99.71 Cul: 84.68 95.67 98.82 99.38 99.71 99.89 99.9999.89 99.99

PC Transformation ..PC Transformation ..

► Loading: the correlation between each band Loading: the correlation between each band and each PC for output interpretation and each PC for output interpretation purposespurposes

ComponentsComponents

Band 1Band 1 2 2 3 3 4 4 5 5 6 6 7 711 0.6490.6490.7260.7260.1990.199-0.014-0.014 0.049 -0.089 -0.008 0.049 -0.089 -0.00822 0.694 0.670 0.178 -0.034 0.004 0.099 0.694 0.670 0.178 -0.034 0.004 0.099 0.1570.157

33 0.785 0.592 0.118 -0.023 -0.018 …. 0.785 0.592 0.118 -0.023 -0.018 …. 4 4 0.894 -0.342 0.287 0.017 …… 0.894 -0.342 0.287 0.017 …… 556677

PCAPCA

PC Transformation ..PC Transformation ..

► Successive components are Successive components are orthogonal, and they are not orthogonal, and they are not correlated to each othercorrelated to each other

► PCs can be used as new bands for PCs can be used as new bands for image classificationimage classification

► PCA is scene specificPCA is scene specific

3.3 Kauth-Thomas Tasseled 3.3 Kauth-Thomas Tasseled CapCap

► An orthogonal transformation An orthogonal transformation ►   The The 4 MSS bands4 MSS bands can be converted can be converted into 4 new bands: into 4 new bands:

brightnessbrightness

greennessgreenness

yellow stuffyellow stuff

non-such non-such

K-T Tasseled CapK-T Tasseled Cap

► SBI = 0.332MSS4 + 0.603MSS5 + 0.675MSS6 + SBI = 0.332MSS4 + 0.603MSS5 + 0.675MSS6 + 0.262MSS70.262MSS7

► GVI = -0.283MSS4 - 0.660MSS5 + 0.577MSS6 + GVI = -0.283MSS4 - 0.660MSS5 + 0.577MSS6 + 0.388MSS70.388MSS7

► YVI = -0.899MSS4 + 0.428MSS5 + 0.0676MSS6 - YVI = -0.899MSS4 + 0.428MSS5 + 0.0676MSS6 - 0.041MSS70.041MSS7

► NSI = -0.016MSS4 + 0.131MSS5 - 0.452MSS6 + NSI = -0.016MSS4 + 0.131MSS5 - 0.452MSS6 + 0.882MSS70.882MSS7

Kauth-Thomas Tasseled CapKauth-Thomas Tasseled Cap

► The first two indices contain the The first two indices contain the most info (90%+) most info (90%+)

►   Brightness is related to bare soils Brightness is related to bare soils ►   Greenness is related to the amount Greenness is related to the amount of green vegetationof green vegetation

Kauth-Thomas Tesseled CapKauth-Thomas Tesseled Cap

Kauth-Thomas Tasseled CapKauth-Thomas Tasseled Cap

► The The 6 TM bands6 TM bands can be converted into can be converted into a 3D space: a 3D space:                  plane of soil plane of soil

plane of vegetationplane of vegetation

and a transition zone and a transition zone ►     A third feature, wetness A third feature, wetness ►     The K-T transformation is The K-T transformation is transferable between scenes transferable between scenes

K-T for TMK-T for TM

► Brightness = 0.33TM1 + 0.33TM2 + 0.55TM3 + Brightness = 0.33TM1 + 0.33TM2 + 0.55TM3 + 0.43TM4 + 0.48TM5 + 0.25TM70.43TM4 + 0.48TM5 + 0.25TM7

► Greenness = -0.25TM1 - 0.16TM2 - 0.41TM3 + Greenness = -0.25TM1 - 0.16TM2 - 0.41TM3 + 0.85TM4 + 0.05TM5 - 0.12TM70.85TM4 + 0.05TM5 - 0.12TM7

► Third = 0.14TM1 + 0.22TM2 - 0.40TM3 + Third = 0.14TM1 + 0.22TM2 - 0.40TM3 + 0.25TM4 0.25TM4

- 0.70TM5 -0.46TM7- 0.70TM5 -0.46TM7

► Fourth = 0.85TM1 - 0.70TM2 - 0.46TM3 - Fourth = 0.85TM1 - 0.70TM2 - 0.46TM3 - 0.003TM4 0.003TM4

- 0.05TM5 - 0.01TM7- 0.05TM5 - 0.01TM7

3.4 IHS3.4 IHS

► Intensity-Hue-Saturation Intensity-Hue-Saturation transformation (IHS)transformation (IHS)

► Transform the RGB space into the IHS Transform the RGB space into the IHS space to represent the informationspace to represent the information

► Intensity: brightnessIntensity: brightness► Hue: colorHue: color► Saturation: purity Saturation: purity

IHSIHS

► The hexcone model projects the RGB The hexcone model projects the RGB cube to a plane, resulting in a cube to a plane, resulting in a hexagon hexagon

► The plane is perpendicular to the The plane is perpendicular to the gray line and tangent to the cube at gray line and tangent to the cube at the "white" cornerthe "white" corner

IHSIHS

► Intensity = distance along the gray Intensity = distance along the gray line from the black point to any line from the black point to any given hexagonal projection given hexagonal projection

► Hue = angle around the hexagon Hue = angle around the hexagon ► Saturation = distance from the gray Saturation = distance from the gray point at the center of  the hexagonpoint at the center of  the hexagon

IHSIHS

►     I,H,S = f(R,G,B)I,H,S = f(R,G,B)

I' = f(I+Ipan)I' = f(I+Ipan) H' = f(H+Hpan)H' = f(H+Hpan) S' = f(S+Span) S' = f(S+Span)

     R',G',B' = f(I',H',S') R',G',B' = f(I',H',S')        

ReadingsReadings

► Chapter 7Chapter 7

PCA ..PCA ..