We propose a novel multi-look synthetic aperture radar image segmentation method that combines Voronoi tessellation

expectation maximization （ EM）

and maximization of the posterior marginal （ MPM） technology. The image domain is partitioned into a group of sub-regions by Voronoi tessellation

each of which is a component of homogeneous regions. Then a multi-look SAR image is modeled on the supposition that the intensities of pixels in each homogenous region satisfy an identical and independent gamma distribution. The image segmentation model is constructed based on the Bayesian paradigm. Finally

the EM/MPM algorithm

which integrates the EM algorithm for model parameter estimation and the MPM algorithm for image segmentation

is implemented. The proposed method expands pixel-based MRF to region-based MRF and achieves optimal segmentation and parameter estimation simultaneously. Results obtained from both real RADARSET-Ⅰ/Ⅱ and simulated SAR intensity images indicate that the proposed method is efficient and promising.