PSO-GA on Endmember extraction for Hyperspectral Imagery
The existing particle swarm optimization (PSO) and genetic algorithms (GA) could not solve some discrete-valued problems effectively such as Endmember extraction in hyperspectral imagery. Firstly, the theory of particle swarm optimization was reviewed, and a genetic algorithm based Endmember extraction method was analyzed, which combined with the convex geometry theory. Then, a particle swarm optimization genetic algorithm (PSO-GA) on Endmember extraction for hyperspectral imagery was proposed, which improves the genetic algorithm with the theory of local best structure of particle swarm optimization. Finally, the experiments were carried out by simulative and real hyperspectral image, and the results between the PSO-GA and GA were compared and analyzed. The results of experiments proved the convergence rate of PSO-GA is much faster than GAs.
hyperspectral particle swarm optimization genetic algorithm endmember extraction
CHEN Wei YU Xu-chu Wang He Wen Bing-gong
Institute of surveying and mapping Information Engineering University Zhengzhou, China Digital LandView Technology Company Limited Beijing, China Troops 78155 Chengdu, China
国际会议
太原
英文
459-464
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）