哈尔滨工程大学学报2012，Vol.33Issue(3)：377-382,6.DOI:10.3969/j.issn.1006-7043.201101044
利用约束非负矩阵分解的高光谱解混算法
Algorithm for hyperspectral unmixing using constrained nonnegative matrix factorization
摘要
Abstract
The existence of mixed pixels impacts the precision advancement of hyperspectral remote sensing application. It is a new research direction to solve the problem of hyperspectral unmixing by nonnegative matrix factorization ( NMF). The nonconvexity of the objective function causes an error to optimal solution in the classic NMF. In this paper, by analyzing the characteristics of endmember signatures and spatial distribution of hyperspectral images , a new approach called minimum covariance and minimum distances nonnegative matrix factorization (MCMD-NMF) was proposed, it is the minimum estimated abundance covariance and minimum the sum of squared distances between all the simplex vertices constrained by the NMF, adopting projected gradient as the iterative learning rule for NMF. MCMDNMF combines the merit of NMF and the characteristics of hyperspectral data, and at the same time, eliminates the pure-pixel assumption. Experimental results demonstrate that the MCMDNMF method can extract the endmember signature and accurately estimate abundance maps.关键词
信息处理技术/高光谱解混/非负矩阵分解/混合像元/丰度Key words
information processing/ hyperspectral unmixing/ nonnegative matrix factorization (NMF) / mixed pixel/ abundance fraction分类
信息技术与安全科学引用本文复制引用
赵春晖,成宝芝,杨伟超..利用约束非负矩阵分解的高光谱解混算法[J].哈尔滨工程大学学报,2012,33(3):377-382,6.基金项目
国家自然科学基金资助项目(61077079) （61077079）
高等学校博士学科点专项科研基金资助基金资助项目(20102304110013) （20102304110013）
哈尔滨市优秀学科带头人基金资助项目(2009RFXXG034). （2009RFXXG034）
