电子学报2013，Vol.41Issue(3)：432-437,6.DOI:10.3969/j.issn.0372-2112.2013.03.003
基于约束非负矩阵分解的高光谱图像解混快速算法
A Fast Algorithm for Hyperspectral Unmixing Based on Constrained Nonnegative Matrix Factorization
摘要
Abstract
Constrained nonnegative matrix factorization was an excellent method for hyperspectral unmixing. The traditional algorithm of this method was based on projected gradient method,and its convergence rate,accuracy and stability needed to be improved . To this end, we considered the excellent minimum volume constraint, and proposed a fast algorithm for hyperspectral unmixing based on constrained nonnegative matrix factorization. First the minimum volume constrained model of the original problem was optimized, then an alternating direction method of multipliers was used to solve the non-convex constrained nonnegative matrix factorization, and at last we modified the iteration steps by singular value decomposition.Experimental results on simulated and real hyperspectral data demonstrate the superiority of the proposed algorithm.关键词
非负矩阵分解/交替方向乘子法/线性光谱解混/最小体积约束Key words
nonnegative matrix factorization(NMF)/ alternating direction method of multipliers/ linear spectral unmixing/ minimum volume constraint分类
信息技术与安全科学引用本文复制引用
刘建军,吴泽彬,韦志辉,肖亮,孙乐..基于约束非负矩阵分解的高光谱图像解混快速算法[J].电子学报,2013,41(3):432-437,6.基金项目
国家自然科学基金(No.61101194,No.61071146) （No.61101194,No.61071146）
江苏省自然科学基金(No.BK20110224) （No.BK20110224）
江苏省博士后科研基金(No.0901008B) （No.0901008B）
中国地质调查局工作项目(No.1212011120227) （No.1212011120227）
