Change detection in SAR images by artificial immune multi-objective clustering

This paper addresses the problem of unsupervised change detection in Synthetic Aperture Radar (SAR) images. Previous approaches have used evolutionary clustering optimization methods, which can suffer from reduced accuracy, because they often use only a single objective function and can easily become trapped at locally optimal values. To overcome these difficulties, we propose a new approach which combines the artificial immune system (AIS) theory with a multi-objective optimization algorithm. First, the self-adaptive artificial immune multi-objective algorithm is adopted to pre-sort the difference image. During this procedure, the difference image is categorized into three classes – changed class, unchanged class and uncertain samples. Second, based on wavelet decomposition to extract features from the difference image, the immune clonal multi-objective clustering algorithm is used to search for the optimal clustering centers of uncertain samples, labeling them as changed or unchanged. Experimental comparisons with four state-of-the-art approaches show that the proposed algorithm can obtain a higher accuracy, is more robust to noise, and finds solutions which are more globally optimal. Additionally, the proposed algorithm can improve the local search ability for the optimal solutions and produces better cluster centers.