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Proceedings Paper

Optimum edge detection in SAR
Author(s): Christopher John Oliver; Ian McConnell; David Blacknell; Richard Geoffrey White
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Paper 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.

Paper Details

Date Published: 21 November 1995
PDF: 12 pages
Proc. SPIE 2584, Synthetic Aperture Radar and Passive Microwave Sensing, (21 November 1995); doi: 10.1117/12.227124
Show Author Affiliations
Christopher John Oliver, Defence Research Agency Malvern (United Kingdom)
Ian McConnell, N.A. Software (United Kingdom)
David Blacknell, Defence Research Agency Malvern (United Kingdom)
Richard Geoffrey White, Defence Research Agency Malvern (United Kingdom)


Published in SPIE Proceedings Vol. 2584:
Synthetic Aperture Radar and Passive Microwave Sensing
Giorgio Franceschetti; Christopher John Oliver; James C. Shiue; Shahram Tajbakhsh, Editor(s)

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