Unsupervised Classification of SAR Images Using Markov Random Fields and GI0 Model

This letter deals with synthetic aperture radar (SAR) data classification in an unsupervised way. Many models have been proposed to fit SAR data (K, Weibull, Log-normal, etc.), but none of them are flexible enough to model all kinds of surfaces (particularly when there are urban areas present in the image). Our main contribution is the application of a statistical model G(0) in a classification process which is shown to be able to model areas with different degrees of heterogeneity. The quality of the classification obtained by mixing this model and a Markovian segmentation is high. We use an iterative conditional estimation method to estimate the parameters of the proposed model.