1. REMOTE SENSING Digital Image Processing Radiometric Enhancement Geometric Enhancement
Reference:
Chapters 4 and 5, Remote Sensing Digital Image Analysis by Richards, J.A. , 1995.
Chapter 6, Remote Sensing by Schowengerdt, R.A.
Professor Ke-Sheng Cheng
Department of Bioenvironmental Systems Engineering
National Taiwan University

2. Radiometric Enhancement
The image DN histogram
5/26/2018
Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

3. Grey level scaling Linear grey level transformations 5/26/2018
Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

4. Logarithmic transformation
Enhance the contrast of dark pixels while reduce the contrast of bright pixels.
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Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

5. Normal transformation
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Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

6. Linear stretch (min-max enhancement)
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Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

7. 5/26/2018
Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

8. 5/26/2018
Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

9. Histogram stretch function (Histogram mapping function)
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Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

10. For continuous histogram and monotonic mapping function
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Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

11. Linear stretch
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Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

12. The above conversion equation applies to continuous histogram and monotonic mapping function only.
For discrete histogram there is no compression effect and the stretch function only changes the locations of grey level bars.
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Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

13. For discrete histogram
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Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

14. 5/26/2018
Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

15. Logarithmic contrast enhancement
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Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

16. Exponential contrast enhancement
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Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

17. Histogram equalization
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Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

18. For discrete histogram
N: total number of pixels L: total number of histogram bins or brightness values k: index for non-zero frequency
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Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

19. 5/26/2018
Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

20. 5/26/2018
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21. Image to image contrast matching (Histogram matching)
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Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

22. 5/26/2018
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23. 5/26/2018
Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

24. Geometric Enhancement
Spatial transformation of pixel grey levels
operate on different scales:
local pixel neighborhood (convolution)
global image (Fourier filters)
all scales (scale-space filters)
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Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

25. An Image Model for Spatial Filtering
Any digital image can be written as the sum of two images,
The Low-Pass (LP) image contains the large area variations, which determines the global image contrast (“macrocontrast”).
The High-Pass (HP) image contains the small area variations, which determines the sharpness and local image contrast (“micro-contrast”).
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Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

26. Example LP and HP image components
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Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

27. The LP and HP image components can be calculated by either convolution or Fourier filters.
Convolution Filters
Local processing within a moving window
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Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

28. Convolution
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Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

29. Low-pass and high-pass filters (LPF, HPF)
3-pixel window with equal weights (moving average) [1/3, 1/3, 1/3]
a complimentary HPF  [-1/3, 2/3, -1/3] =
[0, 1, 0] – [1/3, 1/3, 1/3]
The sum of the LPF weights is one.
The sum of the HPF weights is zero.
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Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

30. 2-D LP and HP Spatial Filters
1⁄9 ⋅
1⁄25
LPF
HPF
3x3
5x5
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Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

31. High-boost filters (HBF)
2-D HB Spatial Filters
1⁄9
The sum of the LPF weights is one.
The sum of the HPF weights is zero.
The sum of the HBF weights is one.
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Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

32. 5/26/2018
Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

33. Directional filters
Emphasize (HP) or suppress (LP) spatial features in a given direction
-1 +1 -1 +1 +2
-1 0
0 +1
Vertical Horizontal Diagonal
0 -1
1 0
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Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

34. Directional enhancements in 3 directions
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Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

35. Statistical Filters
Similar to convolution with moving window, but output is a statistical quantity, e.g.
local minimum, maximum, or median
local standard deviation
local histogram mode (DN at peak)
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Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

36. Gradient Filters for edge detection
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Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.