Remote%20Sensing%20Image%20Enhancement

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Remote SensingImage Enhancement
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Image Enhancement

• Increases distinction between features in a
  scene
• Single image manipulation
• Multi-image manipulation

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Single Image

• Contrast manipulation
• Spatial feature manipulation

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1. Contrast Manipulation

• Gray-level threshold
• Level slicing
• Contrast stretching
• Histogram-equalized stretching

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Contrast Manipulation ..

• Gray-level threshold
• segmenting an image into two classes - binary
  mask
• Level slicing
• dividing the histogram of DNs into several
  slices

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Color-coded temperature maps derived from NIMBUS
http//rst.gsfc.nasa.gov/Sect14/Sect14_4.html
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Contrast Manipulation ..

• Contrast stretching
• Expanding a narrow range of DNs to a full
  range DN - Min
• Linear stretch DN (-------------) 255
• Max - Min
• Advantage simple computation Disadvantage rare
  and frequent values have the same amount of levels

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Stretching
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Contrast Manipulation ..

• Histogram-equalized stretching
• Stretch based on frequency of occurrence
• Frequently occurred DNs have more display
  levels
• Special stretch

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2. Spatial Feature Manipulation

• Spatial filtering
• Edge enhancement
• Convolution
• Directional first differencing

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Spatial Filtering

• Low pass filters emphasize low frequency
  features
• Compute the average values of moving windows

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Low Pass Filter
Mean
Moving windows
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
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Spatial Filtering ..

• High pass filters emphasize local details
• It subtracts the low-pass filter from the
  original image

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Edge Enhancement

• Add back the high frequency image component to
  the original image
• Preserve both the original and the high
  frequency features

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Convolution

• A moving kernel with a weighting factor for each
  pixel

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Convolution

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Directional Differencing

• Displaying the differences in gray levels of
  adjacent pixels
• The direction can be horizontal, vertical, or
  diagonal
• It is necessary to add a constant to the
  difference for display purposes
• Add back the directional difference to the
  original image
• Contrast stretching is needed for all feature
  manipulations

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Convolution

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3. Multi-image Manipulation

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

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3.1 Spectral Ratioing

• A ratio of two bands (with great difference in
  reflectance)
• Useful to eliminate effects of illumination
  differences
• Select bands with distinct spectral responses
• Necessary to stretch the resultant values to a
  full range of DN values after ratioing

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Band Ratioing ..
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Band Ratioing ..
48 31 11 18
48 31 11 18
48 31 11 18
48 31 11 18
.96 .69 .69 .95
.96 .69 .69 .95
.96 .69 .69 .95
.96 .69 .69 .95
50 45 16 19
50 45 16 19
50 45 16 19
50 45 16 19


Band A
Band B
Ratio Band

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

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Hybrid Color Ratio Composite

• Problem different features but of similar ratio
  may appear identical
• Solution when display, combine two ratio bands
  one original band to restore the absolute DN
  values

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3.2 Principle Component Transformation

• To reduce redundancy in multi-spectral data
• The transform
• DNI a11DNA a12DNB a13DNC a14DND
• DNII a21DNA a22DNB a23DNC a24DND
• DNIII a31DNA a32DNB a33DNC a34DND
• DNIV a41DNA a42DNB a43DNC a44DND
• DNI, - DNIV, - DNs in new component images
• DNA, -DND - DNs in the original images
• a11, a12,,,,a44 - coefficients for the
  transformation

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PCA
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PC Transformation ..

• After the axes rotation, the original n bands
  images are converted into n principle components
  images
• The first component (PC1) image contains the
  largest percentage of the total scene variance
  (90)
• The second component (PC2) contains the largest
  of the remaining variance

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PC Transformation ..

• Percentage of variance explained by each
  component
• 84.68 10.99 3.15 0.56 0.33 0.18
  0.10
• Cul 84.68 95.67 98.82 99.38 99.71 99.89
  99.99

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PC Transformation ..

• Loading the correlation between each band and
  each PC for output interpretation purposes
• Components
• Band 1 2 3 4 5 6 7
• 1 0.649 0.726 0.199 -0.014 0.049 -0.089 -0.008
• 2 0.694 0.670 0.178 -0.034 0.004 0.099
  0.157
• 3 0.785 0.592 0.118 -0.023 -0.018 .
• 4 0.894 -0.342 0.287 0.017
• 5
• 6
• 7

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PCA
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PC Transformation ..

• Successive components are orthogonal, and they
  are not correlated to each other
• PCs can be used as new bands for image
  classification
• PCA is scene specific

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3.3 Kauth-Thomas Tasseled Cap

• An orthogonal transformation
•  The 4 MSS bands can be converted into 4 new
  bands brightness
• greenness
• yellow stuff
• non-such

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K-T Tasseled Cap

• SBI 0.332MSS4 0.603MSS5 0.675MSS6
  0.262MSS7
• GVI -0.283MSS4 - 0.660MSS5 0.577MSS6
  0.388MSS7
• YVI -0.899MSS4 0.428MSS5 0.0676MSS6 -
  0.041MSS7
• NSI -0.016MSS4 0.131MSS5 - 0.452MSS6
  0.882MSS7

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Kauth-Thomas Tasseled Cap

• The first two indices contain the most info
  (90)
•  Brightness is related to bare soils
•  Greenness is related to the amount of green
  vegetation

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Kauth-Thomas Tesseled Cap
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Kauth-Thomas Tasseled Cap

• The 6 TM bands can be converted into a 3D space
           plane of soil
• plane of vegetation
• and a transition zone
•   A third feature, wetness
•   The K-T transformation is transferable between
  scenes

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K-T for TM

• Brightness 0.33TM1 0.33TM2 0.55TM3
  0.43TM4 0.48TM5 0.25TM7
• Greenness -0.25TM1 - 0.16TM2 - 0.41TM3
  0.85TM4 0.05TM5 - 0.12TM7
• Third 0.14TM1 0.22TM2 - 0.40TM3 0.25TM4
• - 0.70TM5 -0.46TM7
• Fourth 0.85TM1 - 0.70TM2 - 0.46TM3 - 0.003TM4
• - 0.05TM5 - 0.01TM7

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3.4 IHS

• Intensity-Hue-Saturation transformation (IHS)
• Transform the RGB space into the IHS space to
  represent the information
• Intensity brightness
• Hue color
• Saturation purity

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IHS

• The hexcone model projects the RGB cube to a
  plane, resulting in a hexagon
• The plane is perpendicular to the gray line and
  tangent to the cube at the "white" corner

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IHS

• Intensity distance along the gray line from
  the black point to any given hexagonal projection
  
• Hue angle around the hexagon
• Saturation distance from the gray point at
  the center of  the hexagon

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IHS

•   I,H,S f(R,G,B)
• I' f(IIpan)
• H' f(HHpan)
• S' f(SSpan)
•    R',G',B' f(I',H',S')    

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Readings

• Chapter 7

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PCA ..