Particle Swarm Optimization Algorithm for Unmixing Hyperspectral Image

摘要：

An end-member extraction method for spectral unmixing that is based on Particle Swarm Optimization (PSO) is developed and presented in this paper. The objective function minimized by PSO is the volume of the simplex containing the hyperspectral vectors, following the geometrical characteristics inherent to the data sets. The proposed algorithm has been successfully applied to synthetic hyperspectral image sets, showing to be very fast and be able to determine a high number of endmembers. The experimental results of the proposed algorithm are encouraging. The performance of different versions of PSO is also investigated.

关键词： Endmembers Extraction Hyperspectral Unmixing Particle Swarm Optimization Minimum volume simplex

作者: Mariana Maneiro Xu XiaoJian

作者单位: School of Electronic and Information Engineering Beihang University Beijing 100191, China

会议类型: 国际会议

会议名称: 2010 IEEE 10th International Conference on Signal Processing(第十届信号处理国际会议 ICSP 2010)

会议地点: 北京

会议语种:英文

页码: 897-899

在线出版日期: 2010-08-24（万方平台首次上网日期，不代表论文的发表时间）