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Proceedings Paper

Edge detection in SAR segmentation
Author(s): Christopher John Oliver
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Paper 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.

Paper Details

Date Published: 21 December 1994
PDF: 12 pages
Proc. SPIE 2316, SAR Data Processing for Remote Sensing, (21 December 1994); doi: 10.1117/12.197528
Show Author Affiliations
Christopher John Oliver, Defence Research Agency Malvern (United Kingdom)


Published in SPIE Proceedings Vol. 2316:
SAR Data Processing for Remote Sensing
Giorgio Franceschetti, Editor(s)

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