Segmentation of synthetic-aperture-radar complex data

Abstract

Several algorithms used for segmenting highly speckled high-resolution synthetic-aperture-radar (SAR) complex data into spatially and radiometrically homogeneous regions are presented. The procedure is based on two models, one for the speckled complex amplitudes and the other for the regions. The first model uses the physics of the SAR imaging and processing system to characterize the statistics of speckle, while the second model uses a Markov random field to describe the statistics of the regions. Based on the combination of these two models from Bayes theory, two possible optimality criteria are considered for the segmentation of the complex data into regions. The different algorithms are implemented on a parallel optimization network. Results from both simulated and actual SAR complex data are presented for a comparison of the different alternatives and evaluation of the performance of the segmentation techniques.