Segmentation of multifrequency SAR complex data

Several algorithms for segmenting multifrequency synthetic aperture radar (SAR) complex data into regions of similar and homogeneous backscattering characteristics are presented. The image model is composed of two models, one for the multifrequency complex amplitudes (i.e., speckle), and the other for the region labels. The speckle model is derived from SAR physics. The corresponding analysis illustrates the importance of having a good knowledge of the characteristics of the SAR imaging and processing systems to correctly model the high order statistics of speckle. The region model, on the other hand, uses a multilevel Ising model (a Markov random field) to represent the grouping of pixels into regions. The two models are combined using Bayes´ rule to define an optimal region labeling of the scene given the multifrequency complex amplitudes. Two alternatives are presented that can be implemented on an optimization network. The performance of the segmentation technique is illustrated