Modeling and segmentation of speckled images using complex data

The authors present stochastic models and segmentation algorithms for speckled images, such as synthetic aperture radar (SAR) images. The stochastic model developed is two-level hierarchical random field model which consists of, at the higher level, a Gibbs random field governing the grouping of image pixels into regions, and, at the lower level, speckle processes representing observations in the different regions, which are also modeled as random fields. In accordance with the physical phenomena that cause speckle, the single-look complex speckle process is modeled as a circularly symmetric autocovariance for the complex Gaussian random field, the statistical description of the complex speckle becomes complete. Starting from the model for the single-look complex speckle process, different versions of the model are developed for multilook complex and single- and multilook intensity speckled images. Maximum a posteriori segmentation algorithms using simulated annealing are developed for each of the models corresponding to the single-look and multilook, complex and intensity speckled images