Segmentation-based detection of targets in foliage-penetrating SAR images

Amit Banerjee; Philippe Burlina

doi:10.1117/12.276097

10 June 1997 Segmentation-based detection of targets in foliage-penetrating SAR images

Amit Banerjee, Philippe Burlina

Proceedings Volume 3066, Radar Sensor Technology II; (1997) https://doi.org/10.1117/12.276097
Event: AeroSense '97, 1997, Orlando, FL, United States

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.

Citation Download Citation

Amit Banerjee and Philippe Burlina "Segmentation-based detection of targets in foliage-penetrating SAR images", Proc. SPIE 3066, Radar Sensor Technology II, (10 June 1997); https://doi.org/10.1117/12.276097

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