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 K&I 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.