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Proceedings Paper

Segmentation-based detection of targets in foliage-penetrating SAR images
Author(s): Amit Banerjee; Philippe Burlina
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Paper Abstract

Segmentation and labeling algorithms for foliage penetrating (FOPEN) ultra-wideband Synthetic Aperture Radar (UWB SAR) images are critical components in providing local context in automatic target recognition algorithms. We develop a statistical estimation-theoretic approach to segmenting and labeling the FOPEN images into foliage and non-foliage regions. The labeled maps enable the use of region-adaptive detectors, such as a constant false-alarm rate detector with region-dependent parameters. Segmentation of the images is achieved by performing a maximum a posteriori (MAP) estimate of the pixel labels. By modeling the conditional distribution with a Symmetric Alpha-Stable density and assuming a Markov random field model for the pixel labels, the resulting posterior probability density function is maximized by using simulated annealing to yield the MAP estimate.

Paper Details

Date Published: 10 June 1997
PDF: 10 pages
Proc. SPIE 3066, Radar Sensor Technology II, (10 June 1997); doi: 10.1117/12.276097
Show Author Affiliations
Amit Banerjee, Univ. of Maryland/College Park (United States)
Philippe Burlina, Univ. of Maryland/College Park (United States)


Published in SPIE Proceedings Vol. 3066:
Radar Sensor Technology II
Robert Trebits; James L. Kurtz, Editor(s)

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