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.
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.
LIU Jian-jun, WU Ze-bin, WEI Zhi-hui, XIAO Liang, SUN Le. A Fast Algorithm for Hyperspectral Unmixing Based on Constrained Nonnegative Matrix Factorization. Chinese Journal of Electronics, 2013, 41(3): 432-437.
[1] Iordache M D,Bioucas-dias J M,Plaza A.Sparse unmixing of hyperspectral data.IEEE Transactions on Geoscience and Remote Sensing,2011,49(6):2014-2039. [2] 杨国鹏,周欣,余旭初,陈伟.基于相关向量机的高光谱影像混合像元分解[J].电子学报,2010,38(12):2751-2756. Yang Guo-peng,Zhou Xin,Yu Xu-chu,Chen Wei.Relevance vector machine for hyperspectral imagery unmixing[J].Acta Electronica Sinica,2009,37(9):2016-2023.(in Chinese) [3] Settle J J,Drake N A.Linear mixing and the estimation of ground cover proportions[J].International Journal of Remote Sensing,1993,14(6):1159-1177. [4] Miao L,Qi H.Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix factorization[J].IEEE Transactions on Geoscience and Remote Sensing,2007,45(3):765-777. [5] Winter M E.N-FINDR:An algorithm for fast autonomous spectral end-member determination in hyperspectral data.Proc SPIE Image Spectrometry ⅴ [C].USA:SPIE,1999.266-277. [6] Nascimento J M P,Bioucas-dias J M.Vertex component analysis:A fast algorithm to unmix hyperspectral data[J].IEEE Transactions on Geoscience and Remote Sensing,2005,43(4):898-910. [7] Jun L,Bioucas-dias J M.Minimum volume simplex analysis:A fast algorithm to unmix hyperspectral data [A].IEEE International Geoscience and Remote Sensing Symposium,IGARSS [C].Boston:IEEE,2008.250-253. [8] Bioucas-dias J M.A variable splitting augmented Lagrangian approach to linear spectral unmixing [A].Workshop on Hyperspectral Image and Signal Processing:Evolution in Remote Sensing,WHISPERS [C].Grenoble:IEEE,2009.1-4. [9] Lee D D,Seung H S.Learning the parts of objects by nonnegative matrix factorization[J].Natures,1999,401(6755):788-791. [10] Lin C J.Projected gradient methods for nonnegative matrix factorization[J].Neural Computation.2007,19(10):2756-2779. [11] Gabay D,Mercier B.A dual algorithm for the solution of nonlinear variational problems via finite element approximations[J].Comp and Math Appl,1976,2(1):17-40. [12] Zhang Y.An alternating direction algorithm for nonnegative matrix factorization [R].Houston: Department of Computational and Applied Mathematics,Rice University,2010. [13] Clark R N,US.The US Geological Survey,Digital Spectral Library [DB/OL].http://speclab.cr.usgs.gov/,2007-09-25. [14] Guo Z,Wittman T,Osher S.L1 unmixing and its application to hyperspectral image enhancement [A].Proc SPIE Conference on Algorithms and Technologies for Multispectral,Hyperspectral and Ultraspectral Imagery XV[C].Florida:SPIE,2009.73341M-73341M9.
2013, Vol. 41 
