计算机工程与应用2012，Vol.48Issue(8)：189-193,5.DOI:10.3778/j.issn.1002-8331.2012.08.054
基于粒子群优化的高光谱影像端元提取算法
PSO-based endmembers extraction algorithm for hyperspectral imagery
陈伟 1余旭初 1张鹏强 1王鹤2
作者信息
- 1. 信息工程大学测绘学院,郑州450052
- 2. 北京望神州科技有限公司,北京100020
- 折叠
摘要
Abstract
The theory of particle swarm optimization is reviewed, and two technical ways of endmembers extraction are analyzed. A particle swarm optimization-based endmembers extraction algorithms for hyperspectral imagery is proposed, which is based on the theories of particle swarm optimization, convex geometry and the linear spectral mixture model. The fast implementation method of this algorithm is designed. This algorithm needn' t suppose that there are pure pixels in hyperspectral images., as well as this algorithm can preserve the shape of the endmembers' spectrums. It carries out the experiments by simulative and AVIRIS hyperspectral image, and the results among the PSO-based algorithm, SGA and NMF are compared and analyzed. The results of experiments prove the PSO-based algorithm is more accurate than SGA and NMF.关键词
高光谱影像/粒子群优化/线性混合模型/端元提取Key words
hyperspectral imagery/ particle swarm optimization/ linear mixture model/ endmembers extraction分类
信息技术与安全科学引用本文复制引用
陈伟,余旭初,张鹏强,王鹤..基于粒子群优化的高光谱影像端元提取算法[J].计算机工程与应用,2012,48(8):189-193,5.
