A new texture image segmentation algorithm, HMTseg, was recently proposed and applied successfully to supervised segmentation. In this paper, we extend the HMTseg algorithm to unsupervised SAR image segmentation. A multiscale Expectation Maximization (EM) algorithm is used to integrate the parameter estimation and classification into one. Because of the high levels of speckle noise present at fine scales in SAR images, segmentations on coarse scales are more reliable and accurate than those on fine scales. Based on the Hybrid Contextual Labelling Tree (HCLT) model, a weight factor, β, is introduced to increase the emphasis of context information. Ultimately, a Bayesian interscale and intrascale fusion algorithm is applied to refine raw segmentations.