Frontiers of Optoelectronics, Volume. 9, Issue 4, 627(2016)
Hyperspectral image unmixing algorithm based on endmember-constrained nonnegative matrix factorization
[1] [1] Thouvenin P A, Dobigeon N, Tourneret J Y. Hyperspectral unmixing with spectral variability using a perturbed linear mixing model. IEEE Transactions on Signal Processing, 2016, 64(2): 525–
[2] [2] Heylen R, Scheunders P. A multilinear mixing model for nonlinear spectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(1): 240–251
[3] [3] Zheng C Y, Li H, Wang Q, Chen C L P. Reweighted sparse regression for hyperspectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(1): 479–488
[4] [4] Altmann Y, Pereyra M, Bioucas-Dias J. Collaborative sparse regression using spatially correlated supports—application to hyperspectral unmixing. IEEE Transactions on Image Processing, 2015, 24(12): 5800–5811
[5] [5] Guillamet D, Vitrià J, Schiele B. Introducing a weighted non-negative matrix factorization for image classi.cation. Pattern Recognition Letters, 2003, 24(14): 2447–2454
[6] [6] Pauca V P, Piper J, Plemmons R J. Nonnegative matrix factorization for spectral data analysis. Linear Algebra and Its Applications, 2006, 416(1): 29–47
[7] [7] Miao L, Qi H. Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix factorization. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45 (3): 765–777
[8] [8] Hoyer P O. Non-negative matrix factorization with sparseness constraints. Journal of Machine Learning Research, 2004, 5(1): 1457–1469
[9] [9] Lu X, Wu H, Yuan Y. Double constrained NMF for hyperspectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(5): 2746–2758
[10] [10] Luo W F, Zhong L, Zhang B, Gao L R. Independent component analysis for spectral unmixing in hyperspectral remote sensing image. Spectroscopy and Spectral Analysis, 2010, 30(6): 1628– 1633 (in Chinese)
[11] [11] Wu B, Zhao Y, Zhou X. Unmixing mixture pixels of hyperspectral imagery using endmember constrained nonnegative matrix factor-ization. Computer Engineering, 2008, 34(22): 229–231
[12] [12] Chang C, Du Q. Estimation of number of spectrally distinct signal sources in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(3): 608–619
[13] [13] Heinz D C, Chang C. Fully constrained least squares linear spectral mixture analysis method for material quanti.cation in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(3): 529–545
[14] [14] Gillis N, Glineur F. Using underapproximations for sparse nonnegative matrix factorization. Pattern Recognition, 2010, 43 (4): 1676–1687
[15] [15] Clark R N, Swayze G A. Evolution in imaging spectroscopy analysis and sensor signal-to-noise: an examination of how far we have come. In: Proceedings of The 6th Annual JPL Airborne Earth Science Workshop, 1996
Get Citation
Copy Citation Text
Yan ZHAO, Zhen ZHOU, Donghui WANG, Yicheng HUANG, Minghua YU. Hyperspectral image unmixing algorithm based on endmember-constrained nonnegative matrix factorization[J]. Frontiers of Optoelectronics, 2016, 9(4): 627
Category: RESEARCH ARTICLE
Received: May. 5, 2016
Accepted: Oct. 21, 2016
Published Online: Mar. 9, 2017
The Author Email: Yan ZHAO (zh_ao_yan@sina.com)