An unsupervised segmentation method for synthetic aperture radar (SAR) images is proposed. It alternately approximates the maximization of the posterior marginals estimate of the pixel class labels and estimates all model parameters except the number of classes during segmentation. In this method, a multilevel logistic (MLL) model for the pixel class labels and Gamma distribution for the marginal distribution of each class in the observed SAR image are employed. In our implementation, the expectation-maximization algorithm is used to estimate parameters of the Gamma distributions, and the iterative conditional estimation algorithm is used to estimate the MLL model parameters. The segmentation results for synthetic and real SAR images show that the proposed method has a good performance.
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