Change detection for SAR images based on quantum-inspired immune clonal clustering algorithm
基于量子免疫克隆聚类的SAR图像变化检测

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 propose the change detection for 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, and 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 K&I threshold, it can achieve the better value, and compared with Genetic Algorithm Based Clustering (GAC), the proposed method can search the more better clustering center quickly and effectively, besides, it can detect the accurate edge, improve the change detection accuracy.