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Proceedings Paper

Markov random field segmentation methods for SAR target chips
Author(s): Robert A. Weisenseel; William Clement Karl; David A. Castanon; Gregory J. Power; Phil Douville
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Paper Abstract

DARPA's Moving and Stationary Target Acquisition and Recognition (MSTAR) program has shown that image segmentation of Synthetic Aperture Radar (SAR) imagery into target, shadow, and background clutter regions is a powerful tool in the process of recognizing targets in open terrain. Unfortunately, SAR imagery is extremely speckled. Impulsive noise can make traditional, purely intensity-based segmentation techniques fail. Introducing prior information about the segmentation image -- its expected 'smoothness' or anisotropy -- in a statistically rational way can improve segmentations dramatically. Moreover, maintaining statistical rigor throughout the recognition process can suggest rational sensor fusion methods. To this end, we introduce two Bayesian approaches to image segmentation of MSTAR target chips based on a statistical observation model and Markov Random Field (MRF) prior models. We compare the results of these segmentation methods to those from the MSTAR program. The technique we find by mapping the discrete Bayesian segmentation problem to a continuous optimization framework can compete easily with the MSTAR approach in speed, segmentation quality, and statistical optimality. We also find this approach provides more information than a simple discrete segmentation, supplying probability measures useful for error estimation.

Paper Details

Date Published: 13 August 1999
PDF: 12 pages
Proc. SPIE 3721, Algorithms for Synthetic Aperture Radar Imagery VI, (13 August 1999); doi: 10.1117/12.357662
Show Author Affiliations
Robert A. Weisenseel, Boston Univ. (United States)
William Clement Karl, Boston Univ. (United States)
David A. Castanon, Boston Univ. (United States)
Gregory J. Power, Air Force Research Lab. (United States)
Phil Douville, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 3721:
Algorithms for Synthetic Aperture Radar Imagery VI
Edmund G. Zelnio, Editor(s)

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