Unsupervised Approach Based on MAP and RF to Change Detection in Multitemporal SAR Images

Based on the morphological attribute profile (MAP) and random forest (RF) , an unsuperviesed change detection approach for SAR images was proposed. Firstly, the MAP algorithm was employed to extract the geometric feature of the difference image and a feature vector space was constructed to describe the image inherent structure. Secondly, based on automatic selection of training samples by the combination of thresholding method as well as the offsets, the RF was employed to distinguish changed from unchanged pixels in multidimensional feature vector space. Finally, the mathematic morphology method was used to filter false alarm. Experimental results show that the proposed method can extract the changed area effectively and achieve a better performance than the classical change detection methods based on thresholding.