Markovian modelling and fisher distribution for unsupervised classification of radar images

Statistical segmentation techniques based on hidden Markov field modelling have generated considerable interest in past years. They take contextual information into account in a particularly elegant and rigorous way. Although these models have been thoroughly tested, they can fail in some cases such as the non-stationary one. In this article, we propose use of the recently developed triplet Markov field, which models non-stationary images, and that of Fisher distribution, which is adapted to a wide range of surfaces for modelling synthetic aperture radar (SAR) image noise. Examples illustrate the difference between the approach proposed and classical ones. Various experiments indicate that the new model and its associated unsupervised algorithm perform better than classical ones.