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. 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. 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%. SFLA-DPSO showed a much stronger ability to overcome local convergence than DPSO.