Unsupervised Classification of SAR Images Using Markov Random Fields and ${cal G}_{I}^{0}$ 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 G⁰ 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.