Segmentation is of great importance in the community of synthetic aperture radar (SAR) imaging interpreting and
understanding. In this paper we realize an unsupervised SAR image segmentation system based on statistical maximum a
posterior (MAP) classification criterion and physical heat diffusion derived anisotropic smoothing process. Generalized
mixed Gaussian distribution is applied to model the image gradation with expectation maximize (EM) method
implementing the parameter estimation. A novel idea is proposed to linearly combine the Gaussian branch related
posterior probabilities to fit the segmentation problem size and this endows cursory initial segmentation robust adaption
to a wide range of SAR data variability. Proper use of anisotropic diffusion (AD) on the posterior probability domain can
effectively remove the multiplicative speckle noise of raw data and has advantage to smooth the inner area while well
preserve region edges, just as optimal ultimate segmentation process requires. A brief introduction of the method is
presented along with many application considerations. The correctness and efficiency of the method have been verified
by several examples.
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