This paper introduces Bayesian supervised and unsupervised segmentation
algorithms aimed at oceanic segmentation of SAR images. The data term,
\emph{i.e}., the density of the observed backscattered signal given the region,
is modeled by a finite mixture of Gamma densities with a given predefined
number of components. To estimate the parameters of the class conditional
densities, a new expectation maximization algorithm was developed. The prior is
a multi-level logistic Markov random field enforcing local continuity in a
statistical sense.