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13 August 1999 Markov random field segmentation methods for SAR target chips
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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.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Robert A. Weisenseel, William Clement Karl, David A. Castanon, Gregory J. Power, and Phil Douville "Markov random field segmentation methods for SAR target chips", Proc. SPIE 3721, Algorithms for Synthetic Aperture Radar Imagery VI, (13 August 1999);

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