Multivariate Fuzzy Hidden Markov Chains Model Applied to Unsupervised Multiscale SAR Image Segmentation

This paper deals with unsupervised segmentation of multicomponent images. In order to address the classification issue, we propose to use a new rectorial fuzzy version of hidden Markov chains (HMC). The main characteristic of the proposed model is to simultaneously use Dirac and Lebesgue measures at the class chain level. It then allows the coexistence of crisp pixels (obtained with the uncertainty measure of the model) and fuzzy pixels (obtained with the fuzzy measure of the model) in the same image. Crisp and fuzzy multidimensional densities can then be estimated in the segmentation process, according to the assumption considered to model the statistical links between the layers of the multiband image. The efficiency of the proposed method is illustrated with a multiscale decomposition of a synthetic aperture radar (SAR) image and comparisons with one-dimensional fuzzy HMC are also provided. The segmentation results show the interest of the new method