[6] [6] Lee D D, Seung H S. Learning the parts of objects by non-negative matrix factorization[J]. Nature, 1999, 401(6755): 788-791.

[7] [7] Qian Y, Jia S, Zhou J, et al. Hyperspectral unmixing via L1/2 sparsity-constrained nonnegative matrix factorization[J]. IEEE Transactions on Geoence and Remote Sensing, 2011, 49(11): 4282-4297.

[8] [8] Lu X, Wu H, Yuan Y, et al. Manifold regularized sparse NMF for hyperspectral unmixing[J]. IEEE Transactions on Geoscience & Remote Sensing, 2013, 51(5): 2815-2826.

[9] [9] Zuoyu Z, Shouyi L, Hexin Z, et al. Bilateral filter regularized l2 sparse nonnegative matrix factorization for hyperspectral unmixing[J]. Remote Sensing, 2018, 10(6): 816.

[10] [10] Zhang S, Li J, Li H C, et al. Spectral-spatial weighted sparse regression for hyperspectral image unmixing[J]. IEEE Transactions on Geoscience & Remote Sensing, 2018, 56(6): 3265-3276.

[11] [11] He W, Zhang H, Zhang L. Total variation regularized reweighted sparse nonnegative matrix factorization for hyperspectral unmixing [J]. IEEE Transactions on Geoscience & Remote Sensing, 2017, 55(7): 3909-3921.

[13] [13] Emmanuel J. Candès, Wakin M B , Boyd S P . Enhancing Sparsity by Reweighted l1 Minimization[J]. Journal of Fourier Analysis & Applications, 2008, 14(5-6): 877-905.

[14] [14] Zhu F, Halimi A, Honeine P, et al. Correntropy maximization via ADMM: application to robust hyperspectral unmixing [J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(9): 4944-4955.

[15] [15] Iordache M D, Bioucas-Dias J M, Plaza A. Collaborative sparse regression for hyperspectral unmixing[J]. IEEE Transactions on Geoence and Remote Sensing, 2014, 52(1 Part1): 341-354.

[16] [16] 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: 765-777.

[18] [18] Su Y, Li J, Plaza A, et al. DAEN: Deep auto-encoder networks for hyperspectral unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019: 10.1109/tgrs.2018.2890633.