Changepoint detection using reversible jump MCMC methods

This paper addresses the problem of SAR image segmentation by using reversible jump MCMC sampling. The SAR image segmentation problem is formulated as a Bayesian estimation problem. The reversible jump MCMC algorithm is then used to generate samples distributed according to the joint posterior distribution of the unknown parameters. These samples allow to compute marginal maximum a posteriori estimates for the interesting features. The performance of the proposed methodology is illustrated via several simulation results.