关键词: 粒子群算法, 端元提取, 高光谱遥感

Abstract: Particle Swarm Optimization（PSO） is an optimization algorithm based on continuous space. Because the number of endmembers is small and endmembers are scattered in the hyperspectral image, the search space of the PSO is scattered. The traditional PSO algorithm has the weaknesses of being sensitive to initial value, low convergency, easy to fall into the local optimum. To solve this problem, this paper selects the pixels with high Pure Pixel Index（PPI） as the preselected pixels, and sorts the preselected pixels. Finally, the sorting pixels are taken as the searching space of PSO, to reduce the search space and improve the efficiency of the algorithm. Experimental results show that this algorithm has better result than other algorithms in simulating and AVIRIS images.

Key words: particle swarm optimization, endmember extraction, hyperspectral remote sensing