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

作　　者：陈伟[1] 余旭初[1] 张鹏强[1] 王鹤 

机构地区：[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

出　　处：《计算机工程与应用》2012年第8期189-193,共5页Computer Engineering and Applications

摘　　要：回顾了粒子群算法的基本原理,分析了端元提取算法的两种技术途径。利用粒子群优化的原理,结合凸面几何学理论和线性光谱混合模型,设计了一种粒子群优化端元提取算法,并设计了算法的快速实现方法。该算法不需要假设影像中存在纯像元,同时保持了端元光谱的形状。利用模拟数据和AVIRIS影像对该算法、SGA算法和NMF算法进行实验对比分析,实验结果证明该算法的端元提取精度优于其他二者。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 theo- ries of particle swarm optimization, convex geometry and the linear spectral mixture model. The fast implementation method of this al- gorithm is designed. This algorithm needn＇t suppose that there are pure pixels in hyperspectral images, as well as this algorithm can pre- serve 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 al- gorithm 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 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]