21 December 1994 Edge detection in SAR segmentation
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In this paper we discussed problems associated with segmentation based on edge detection by performing a least-squares fit to either the local mean or texture of a SAR image. An important stage in the discussion is the extent to which this algorithm represents an optimum process. We therefore study typical statistical properties of a SAR image of the Amazon rain forest and establish corresponding optimum estimators. We demonstrate that the amplitude is not far from optimum for segmenting the mean by least-squares fitting while both the normalized log of the intensity and the amplitude contrast approximate a maximum likelihood texture measure. We next compare the statistics of these measures with equivalent Gaussians to establish the extent to which a least-squares fit represents the maximum likelihood method for determining edge height and position. Finally theoretical predictions are compared with texture segmentation results on the rain forest example.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Christopher John Oliver, Christopher John Oliver, } "Edge detection in SAR segmentation", Proc. SPIE 2316, SAR Data Processing for Remote Sensing, (21 December 1994); doi: 10.1117/12.197528; https://doi.org/10.1117/12.197528


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