As classical change detection methods for Synthetic Aperture Radar (SAR) images have high error rates and low detection rates, a novel change detection method of SAR images based on Morphology Attribute Profile (MAP) was proposed. The MAP algorithm was employed to extract the geometric features of the difference images and a feature vector space was constructed to describe the image inherent structure. Then, the offsets were introduced to select the training samples automatically based on the segmentation of different images by using thresholding method. Finally, Support Vector Machine (SVM) was used to distinguish changed pixels from unchanged pixels in the multidimensional feature space. Experiment results show that the proposed method achieves better performance than the KI threshold selection criterion based on Gaussian model (GM_KI), KI threshold selection criterion based on general Gaussian model(GGM_KI) and Otsu methods, the lowest Kappa is 0.87, and the lowest anti-noise is 0.97 when the Peak Signal to Noise Ratio(PSNR) belongs to ［29,44］dB. These results verify the effectiveness and superiority of the proposed method.