Abstract
Several algorithms for segmenting single look Synthetic Aperture Radar (SAR) complex data into regions of similar and homogeneous statistical characteristics are presented. The image model is composed of two models, one for the speckled complex amplitudes, and the other for the region labels. Speckle is modeled from the physics of the SAR imaging and processing system, and region labels are represented as a Markov random field. Based on this composite image model, an approximation to the Maximum A Posteriori (MAP) and the Maximum Posterior Marginal (MPM) estimates of the region labels is computed and implemented using a parallel optimization network. The performance of this algorithm on highly speckled fine resolution SAR data is discussed and illustrated using both simulated and actual SAR complex data.
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