Abstract：Based on the clonal selection optimization with quantum crossover,a novel Quantum Clonal Optimization Algorithm is proposed for solving SAR image classification problems,theoretical analysis based on the theory of Markov has proved that the new algorithm could converage to the global optimum.The new algorithm can carry out searching in many directions around the same antibody simultaneously.The proposed quantum crossover operator realizes the information interactions among the sub-population so as to prevent premature convergence effectively.The experimental results on X-band and Ku-band SAR images indicate that compared with the Fuzzy C-means algorithm,the K-Nearest Neighbor algorithm,and the Clonal Selection Algorithm,the average correct rate of the new algorithm is improved by 13.57%,11.79% and 5.79%,and the robust of the new algorithm also outperforms the other three methods.
马文萍;焦李成;张向荣;李阳阳. 基于量子克隆优化的SAR图像分类[J]. 电子学报, 2007, 35(12): 2241-2246.
MA Wen-ping;JIAO Li-Cheng;ZHANG Xiang-Rong;LI Yang-yang. SAR Image Classification Based on Quantum Clonal Optimization. Chinese Journal of Electronics, 2007, 35(12): 2241-2246.
2007, Vol. 35 
