Abstract
As the conventional evolutionary clustering optimization methods are often time-consuming and easy to trap in local optimal value in dealing with the problem of change detection.Furthermore,it can not detect the edge accurately for SAR images.We proposed a method for change detection in SAR images based on the clustering analysis.The proposed method takes gray-levels as an input,uses the quantum bit to define the clustering center,searches the optimal cluster center using the quantum-inspired immune clonal algorithm,and gets the global threshold.Finally,the change-detection map is produced.Compared with KI threshold,it can achieve a better value.Compared with Genetic Algorithm Based Clustering(GAC),the proposed method can search a much better clustering center quickly and effectively.Besides,it can detect the accurate edge and improve the change detection accuracy.
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