Abstract. To achieve the optimal image segmentation, an energy segmentation method based on multiple features and blocks for high resolution Synthetic Aperture Radar (SAR) image is proposed in this paper. First of all, a feature vector of pixel is formed with the texture feature extracted by curvelet transform and means function, the boundary feature extracted by curvelet transform and Canny Operator, and the original spectral feature; a feature set is formed by all feature vectors of pixels in the image. The feature vector is considered as segmentation basis, and its domain is partitioned by regular tessellation. On the partitioned image domain, a label variable is assigned to a regular block; each homogeneous region is fitted by one or more regular blocks; Obviously, a label field is formed by all the label variables of regular blocks. The model of label field is built by using energy function of neighborhood relationship. The feature set is considered as a realization of a random filed of multiple features (multiple features field for short). A heterogeneous energy function is used to establish the model of multiple features field. Then the established models of the label field and multiple features field are combined to define global energy function of image segmentation, and non-constrained Gibbs probability distribution is used to describe the global energy function to build the energy segmentation model based on multiple features. Further, a RJMCMC algorithm is designed to simulate from the model to segment SAR image. To verify the feasibility and superiority of the proposed approach, real SAR images are tested.