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21 December 1994 Edge detection in SAR segmentation
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Abstract
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 "Edge detection in SAR segmentation", Proc. SPIE 2316, SAR Data Processing for Remote Sensing, (21 December 1994); https://doi.org/10.1117/12.197528
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