目的 针对离散粒子群优化(D-PSO)端元提取算法易“早熟”,易陷入局部最优解等问题,引入蛙跳算法,提出了基于蛙跳算法的离散粒子群优化(SFLA-DPSO)端元提取算法.方法 该算法把粒子群分成若干族群,先在每个族群内进行深度寻优,然后在族群间完成信息交流,实现了SFLA算法的全局性、并行性与D-PSO算法的快速收敛性相结合,进而避免粒子陷入局部最优解.分别用SFLA-DPSO、D-PSO和SMACC对云南普朗地区Hperion高光谱影像提取端元;同时,在Hperion和AVIRIS高光谱影像的可行解搜索空间内,分别用SFLA-DPSO、D-PSO和N-FINDR提取端元,借助统计学理论分析计算两种算法在不同迭代次数下达到全局收敛的概率.结果 当达到一定迭代次数后,SFLA-DPSO出现全局收敛的概率基本达到100%,而D-PSO却仅在65%左右,因此SFLA-DPSO算法具有较高的可信度.结论 从而认为SFLA-DPSO克服局部收敛的能力更强,表现出良好的稳定性.

Objective The problem of easily falling into “premature” and local optimum values is encountered during endmember extraction via discrete particle swarm optimization (DPSO). Thus, this study introduced the shuffled frog leaping algorithm (SFLA) into DPSO. When SFLA-DPSO (DPSO based on SFLA) was run, the particle swarm was divided into several groups. DPSO was then used for an in-depth search of endmembers, and information was exchanged between groups. SFLA-DPSO combined the universality and parallelism of SFLA with the fast convergence of DPSO. The combined method can prevent the particle swarm from falling in the local optimum.Method SFLA-DPSO, DPSO, and SMACC were applied to extract the endmembers of a hyperspectral RS image in Pulang, Yunnan Province. Then, the probability of global convergence was calculatedby using SFLA-DPSO, DPSO, and N-FINDR to extract the endmembers in the respective search space of the Hyperion and AVIRIS hyperspectral images.Result SFLA-DPSO had higher precision than the other tested methods. The global convergence probability of SFLA-DPSO reached 100% as the number of iterations increased, whereas that ofDPSO was only between 50% and 65%.Conclusion SFLA-DPSO showed a much stronger ability to overcome local convergence than DPSO.