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21 November 1995 Optimum edge detection in SAR
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Abstract
In this paper we derive the maximum likelihood (ML) criterion for splitting (or merging) two regions of single-look SAR imagery as a function of the mean intensity. Two distinct optimization criteria can be postulated: (1) maximizing the total probability of detecting an edge within a window; and (2) maximizing the accuracy with which the edge position can be determined. Initially we derive the ML solution for the first criterion and demonstrate its superiority over an approach based on the Student t test when applied to intensity segmentation. Next we discuss the ML solution for determining the edge position. Finally, we propose a two-stage edge detection scheme offering near optimum edge detection and position estimation.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Christopher John Oliver, Ian McConnell, David Blacknell, and Richard Geoffrey White "Optimum edge detection in SAR", Proc. SPIE 2584, Synthetic Aperture Radar and Passive Microwave Sensing, (21 November 1995); https://doi.org/10.1117/12.227124
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