南京理工大学学报（自然科学版）2011，Vol.35Issue(4)：431-435,5.
基于迭代加权L1正则化的高光谱混合像元分解
Hyperspectral Unmixing Based on Iterative Weighted L1 Regularization
摘要
Abstract
In order to improve the accuracy of hyperspectral unmixing, linear unmixing based on sparsity is studied. A novel method of linear hyperspectral unmixing based on iterative weighted LI regularization is proposed,and the corresponding model and algorithm are presented. The method introduces several steps of weighted LI optimization procedures,and uses the value of current solution to revise the weights for next iteration, which makes the sparsity of fractional abundances of mixed pixel be represented better. Experimental results demonstrate that the accuracy of hyperspectral unmixing based on iterative weighted LI is higher than traditional LI regularization, especially for high signal-to-noise ratio hyperspectral images.关键词
高光谱/混合像元分解/迭代加权/正则化Key words
hyperspectral image/unmixing/iterative weighting/LI regularization分类
信息技术与安全科学引用本文复制引用
吴泽彬,韦志辉,孙乐,刘建军..基于迭代加权L1正则化的高光谱混合像元分解[J].南京理工大学学报（自然科学版）,2011,35(4):431-435,5.基金项目
国家自然科学基金(61101194,61071146) （61101194,61071146）
中国地质调查局工作项目(1212011120227) （1212011120227）
航遥中心对地观测技术工程实验室开放课题 （）
