In this paper, a segmentation method for synthetic aperture radar(SAR) images based on Kullback-Leibler(KL) distance and regular tessellation is proposed. Firstly, the image domain is divided into several sub-blocks by a regular tessellation, and the divided blocks are considered as basic processing units during segmentation. It is assumed that all pixels in a sub-block follow Gaussian distribution, while to modeling feature field of a given image. Then a general Potts model is utilized to model relationship between neighbor sub-blocks in label field. According to Bayes theorem, the posterior probability model is obtained by combining pixels' feature and sub-blocks' labels. Thereafter, the heterogeneity coefficient between classes is characterized with KL distance, and the corresponding probability distribution function is constructed by a non-constrained Gibbs distribution. Combining the posterior probability model and the non-constrained Gibbs distribution, the image segmentation model is established. In order to simulate the segmentation model, a Metropolis-Hastings(M-H) sampling method is designed, including the operations of changing label and splitting sub-blocks. By analyzing the segmentation results of the proposed algorithm and the comparing algorithms, the validity and superiority of the proposed algorithm are fully verified.