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.