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Remote Sensing Image Enhancement

Remote Sensing Image Enhancement. Image Enhancement. Increases distinction between features in a scene Single image manipulation Multi-image manipulation. Single Image. Contrast manipulation Spatial feature manipulation. 1. Contrast Manipulation. Gray-level threshold Level slicing

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Remote Sensing Image Enhancement

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  1. Remote SensingImage Enhancement

  2. Image Enhancement • Increases distinction between features in a scene • Single image manipulation • Multi-image manipulation

  3. Single Image • Contrast manipulation • Spatial feature manipulation

  4. 1. Contrast Manipulation • Gray-level threshold • Level slicing • Contrast stretching • Histogram-equalized stretching

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

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

  7. 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

  8. Stretching

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

  10. 2. Spatial Feature Manipulation • Spatial filtering • Edge enhancement • Convolution • Directional first differencing

  11. Spatial Filtering • Low pass filters emphasize low frequency features • Compute the average values of moving windows

  12. Low Pass Filter Moving windows Mean 3 4 5 0 1 6 8 3 1 5 3 4 0 2 1 3 8 0 5 1 432 4 4 2

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

  14. Edge Enhancement • Add back the high frequency image component to the original image • Preserve both the original and the high frequency features

  15. Convolution • A moving kernel with a weighting factor for each pixel

  16. Convolution

  17. 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

  18. Convolution

  19. 3. Multi-image Manipulation • Spectral ratioing • Principle component transformation • Kauth-Thomas tasseled cap • Intensity-Hue-Saturation transformation (IHS)

  20. 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

  21. Band Ratioing ..

  22. Band Ratioing .. • 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 ÷ = Band A ÷ Band B = Ratio Band

  23. 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

  24. 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

  25. PCA

  26. 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

  27. 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

  28. 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

  29. PCA

  30. 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

  31. 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

  32. 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

  33. 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

  34. Kauth-Thomas Tesseled Cap

  35. 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

  36. 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

  37. 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

  38. 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

  39. 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

  40. IHS •   I,H,S = f(R,G,B) I' = f(I+Ipan) H' = f(H+Hpan) S' = f(S+Span)    R',G',B' = f(I',H',S')

  41. Readings • Chapter 7

  42. PCA ..

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