Multisensor image segmentation using Dempster-Shafer fusion in Markov fields context

This paper deals with the statistical segmentation of multisensor images. In a Bayesian context, the interest of using hidden Markov random fields, which allows one to take contextual information into account, has been well known for about 20 years. In other situations, the Bayesian framework is insufficient and one must make use of the theory of evidence. The aim of the authors' work is to propose evidential models that can take into account contextual information via Markovian fields. They define a general evidential Markovian model and show that it is usable in practice. Different simulation results presented show the interest of evidential Markovian field model-based segmentation algorithms. Furthermore, an original variant of generalized mixture estimation, making possible the unsupervised evidential fusion in a Markovian context, is described. It is applied to the unsupervised segmentation of real radar and SPOT images showing the relevance of the proposed models and corresponding segmentation methods in real situations.