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

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.

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.

Logarithmic transformation Enhance the contrast of dark pixels while reduce the contrast of bright pixels. 5/26/2018 Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

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

Linear stretch (min-max enhancement) 5/26/2018 Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

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

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

Histogram stretch function (Histogram mapping function) 5/26/2018 Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

For continuous histogram and monotonic mapping function 5/26/2018 Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

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

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

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

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

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

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

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

For discrete histogram N: total number of pixels L: total number of histogram bins or brightness values k: index for non-zero frequency 5/26/2018 Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

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

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

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

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

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

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

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

Example LP and HP image components 5/26/2018 Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

The LP and HP image components can be calculated by either convolution or Fourier filters. Convolution Filters Local processing within a moving window 5/26/2018 Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

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

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

2-D LP and HP Spatial Filters 1⁄9 +1 +1 +1 ⋅ -1 -1 -1 -1 +8 -1 1⁄25 +1 +1 +1 +1 +1 -1 -1 -1 -1 -1 -1 -1 24 -1 -1 LPF HPF 3x3 5x5 5/26/2018 Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

High-boost filters (HBF) 2-D HB Spatial Filters 1⁄9 -2 -2 -2 -2 25 -2 -1 -1 -1 -1 17 -1 -3 -3 -3 -3 33 -3 The sum of the LPF weights is one. The sum of the HPF weights is zero. The sum of the HBF weights is one. 5/26/2018 Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

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

Directional filters Emphasize (HP) or suppress (LP) spatial features in a given direction -1 +1 -1 +2 -1 -1 +1 +2 -1 0 0 +1 -1 0 0 0 2 0 0 0 -1 Vertical Horizontal Diagonal 0 -1 1 0 5/26/2018 Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

Directional enhancements in 3 directions 5/26/2018 Laboratory for Remote Sensing Hydrology and Spatial Modeling, Dept of Bioenvironmental Systems Engineering, National Taiwan Univ.

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

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