Change Detection in Synthetic Aperture Radar Images Based on Evolutionary Multiobjective Optimization with Ensemble Learning


This paper presents an unsupervised change detection approach for synthetic aperture radar (SAR) images based on a multiobjective clustering algorithm and selective ensemble strategy. A multiobjective clustering method based on the nondominated neighbor immune algorithm is proposed for classifying changed and unchanged regions in the difference image, which aims at reducing the effect of speckle noise and enhancing the cluster performance. The proposed multiobjective clustering method generates a set of mutually intermediate clustering solutions, which correspond to different trade-offs between the two objectives: restraining noise and preserving detail. Then the selective ensemble strategy is introduced to integrated theses intermediate change detection results. Experiments on real SAR images show that the proposed change detection method based on multiobjective clustering reduces the effect of speckle noise and enhancing the cluster performance. In general, the proposed method makes a balance between noise-immunity and the preservation of image detail. The final change detection results obtained by the selective ensemble strategy exhibit lower errors than other existing methods.