[1] J J WEI, X F WANG. An overview on linear unmixing of hyperspectral data. Mathematical Problems in Engineering, 2020, 3735403(2020).

[2] M J KHAN, H S KHAN, A YOUSAF et al. Modern trends in hyperspectral image analysis： a review. IEEE Access, 6, 14118-14129(2018).

[3] R L PU. Mapping tree species using advanced remote sensing technologies： a state-of-the-art review and perspective. Journal of Remote Sensing, 2021, 9812624(2021).

[4] A R HUETE, T MIURA, X GAO. Land cover conversion and degradation analyses through coupled soil-plant biophysical parameters derived from hyperspectral EO-1 Hyperion. IEEE Transactions on Geoscience and Remote Sensing, 41, 1268-1276(2003).

[5] C GENDRIN, Y ROGGO, C COLLET. Pharmaceutical applications of vibrational chemical imaging and chemometrics： a review. Journal of Pharmaceutical and Biomedical Analysis, 48, 533-553(2008).

[6] W HE, H Y ZHANG, L P ZHANG. Sparsity-regularized robust non-negative matrix factorization for hyperspectral unmixing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9, 4267-4279(2016).

[7] A F GOETZ, G VANE, J E SOLOMON et al. Imaging spectrometry for Earth remote sensing. Science, 228, 1147-1153(1985).

[8] P GHAMISI, N YOKOYA, J LI et al. Advances in hyperspectral image and signal processing： a comprehensive overview of the state of the art. IEEE Geoscience and Remote Sensing Magazine, 5, 37-78(2017).

[9] J M BIOUCAS-DIAS, A PLAZA. An overview on hyperspectral unmixing： geometrical， statistical， and sparse regression based approaches, 1135-1138(2011).

[10] A BEKIT, C I CHANG, B LAMPE et al. N-FINDER for finding endmembers in compressively sensed band domain. IEEE Transactions on Geoscience and Remote Sensing, 58, 1087-1101(2020).

[11] M E WINTER. Comparison of approaches for determining end-members in hyperspectral data, 305-313(2000).

[12] J LI, A AGATHOS, D ZAHARIE et al. Minimum volume simplex analysis： a fast algorithm for linear hyperspectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 53, 5067-5082(2015).

[13] J M BIOUCAS-DIAS. A variable splitting augmented lagrangian approach to linear spectral unmixing, 1-4(2009).

[14] Z W SHI, W TANG, Z N DUREN et al. Subspace matching pursuit for sparse unmixing of hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 52, 3256-3274(2014).

[15] R WANG, H C LI, A PIZURICA et al. Hyperspectral unmixing using double reweighted sparse regression and total variation. IEEE Geoscience and Remote Sensing Letters, 14, 1146-1150(2017).

[16] S Q ZHANG, J LI, H C LI et al. Spectral–spatial weighted sparse regression for hyperspectral image unmixing. IEEE Transactions on Geoscience and Remote Sensing, 56, 3265-3276(2018).

[17] J S BHATT, M V JOSHI. Deep learning in hyperspectral unmixing： a review, 2189-2192(2020).

[18] Q W JIN, Y MA, F FAN et al. Adversarial autoencoder network for hyperspectral unmixing. IEEE Transactions on Neural Networks and Learning Systems, 34, 4555-4569(2023).

[19] L R GAO, Z HAN, D F HONG et al. CyCU-net： cycle-consistency unmixing network by learning cascaded autoencoders. IEEE Transactions on Geoscience and Remote Sensing, 60, 5503914(2022).

[20] S JIA, Y T QIAN. Spectral and spatial complexity-based hyperspectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 45, 3867-3879(2007).

[21] W H WANG, Y T QIAN, Y Y TANG. Hypergraph-regularized sparse NMF for hyperspectral unmixing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9, 681-694(2016).

[22] X R FENG, H C LI, J LI et al. Hyperspectral unmixing using sparsity-constrained deep nonnegative matrix factorization with total variation. IEEE Transactions on Geoscience and Remote Sensing, 56, 6245-6257(2018).

[23] C I CHANG, Q DU. Estimation of number of spectrally distinct signal sources in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 42, 608-619(2004).

[24] D D LEE, H S SEUNG. Learning the parts of objects by non-negative matrix factorization. Nature, 401, 788-791(1999).

[25] Y T QIAN, S JIA, J ZHOU et al. Hyperspectral unmixing via $L_1/2$ sparsity-constrained nonnegative matrix factorization. IEEE Transactions on Geoscience and Remote Sensing, 49, 4282-4297(2011).

[26] R RAJABI, H GHASSEMIAN. Spectral unmixing of hyperspectral imagery using multilayer NMF. IEEE Geoscience and Remote Sensing Letters, 12, 38-42(2015).

[27] R RAJABI, H GHASSEMIAN. Multilayer structured nmf for spectral unmixing of hyperspectral images, 1-4(2014).

[28] L DONG, Y YUAN, X LUXS. Spectral-spatial joint sparse NMF for hyperspectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 59, 2391-2402(2021).

[29] R WANG, H C LI, W Z LIAO et al. Double Reweighted Sparse Regression for Hyperspectral Unmixing, 6986-6989(2016).

[30] L S YANG, J H PENG, H W SU et al. Combined nonlocal spatial information and spatial group sparsity in NMF for hyperspectral unmixing. IEEE Geoscience and Remote Sensing Letters, 17, 1767-1771(2020).

[31] X C LV, W H WANG, H F LIU. Cluster-wise weighted NMF for hyperspectral images unmixing with imbalanced data. Remote Sensing, 13, 268(2021).

[32] C Z DENG, Y G CHEN, S Q ZHANG et al. Robust dual spatial weighted sparse unmixing for remotely sensed hyperspectral imagery. Remote Sensing, 15, 4056(2023).

[33] M D IORDACHE, J M BIOUCAS-DIAS, A PLAZA. Sparse unmixing of hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 49, 2014-2039(2011).

[34] J T PENG, W W SUN, H C LI et al. Low-Rank and Sparse Representation for Hyperspectral Image Processing： a review. IEEE Geoscience and Remote Sensing Magazine, 10, 10-43(2022).

[35] D C HEINZ. Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 39, 529-545(2001).

[36] J M P NASCIMENTO, J M B DIAS. Vertex component analysis： a fast algorithm to unmix hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 43, 898-910(2005).

[37] F ZHU. Spectral unmixing datasets with ground truths(2017).

[38] Z H GUO, T WITTMAN, S OSHER. L1 unmixing and its application to hyperspectral image enhancement. Hyperspectral(2009).

[39] P DE HANDSCHUTTER, N GILLIS, X SIEBERT. Deep matrix factorizations. arXiv, 2010-00380(2020). http：//arxiv.org/abs/2010.00380

[40] X H WANG, M ZHAO, J CHEN. Hyperspectral unmixing via plug-and-play priors, 1063-1067(2020).