[1] [1] HUANG L P. Research progress in the application of hyperspectral remote sensing in crop growth monitoring［J］. Rural Economy and Science Technology, 2019, 30(5): 42-44(in Chinese).

[2] [2] PREY L, von BLOH M, SCHMIDHALTER U. Evaluating RGB imaging and multispectral active and hyperspectral passive sensing for a-ssessing early plant vigor in winter wheat［J］. Sensors, 2018, 18(9): 2931-2942.

[3] [3] YANG G, TIAN Zh N, LI H, et al. Research of target detection of hyperspectral images in complex background based on linear unmixing［J］. Laser Technology, 2020, 44(2): 143-147(in Chinese).

[4] [4] JIANG T X. Overview of the development and application of hyperspectral remote sensing technology in the field of geology［J］. World Nonferrous Metals, 2018, 12(23): 254-255(in Chinese).

[5] [5] YE Ch M, LI Y, CUI P, et al. Design and implementation of hyperspectral remote sensing geological disaster information extraction system［J］. The Chinese Journal of Geological Hazard and Control, 2018, 29(5): 89-94(in Chinese).

[6] [6] BIOUCAS-DIAS J M, PLAZA A, DOBIGEON N, et al. Hyperspectral unmixing overview: Geometrical, statistical, and sparse regre-ssion-based approaches［J］. IEEE Journal of Selected Topics in A-pplied Earth Observations and Remote Sensing, 2012, 5(2): 354-379.

[7] [7] IORDACHE M, BIOUCAS-DIAS J M, PLAZA A. Sparse unmixing of hyperspectral data［J］. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(6): 2014-2039.

[8] [8] IORDACHE M, BIOUCAS-DIAS J M, PLAZA A. Collaborative sparse regression for hyperspectral unmixing［J］. IEEE Transactions on Geoscience and Remote Sensing, 2013, 52(1): 341-354.

[9] [9] ZHENG C Y, LI H, WANG Q, et al. Reweighted sparse regression for hyperspectral unmixing［J］. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(1): 479-488.

[10] [10] WANG R, LI H C, LIAO W, et al. Double reweighted sparse regression for hyperspectral unmixing［C］// IGARSS 2016-2016 IEEE International Geoscience and Remote Sensing Symposium. New York, USA: IEEE, 2016: 6986-6989.

[11] [11] SHI Z, SHI T, ZHOU M, et al. Collaborative sparse hyperspectral unmixing using l0 norm［J］. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(9): 5495-5508.

[12] [12] IORDACHE M, BIOUCAS-DIAS J M, PLAZA A. Total variation spatial regularization for sparse hyperspectral unmixing［J］. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(11): 4484-4502.

[13] [13] LI X, HUANG J, DENG L, et al. Bilateral filter based total variation regularization for sparse hyperspectral image unmixing［J］. Information Sciences, 2019, 504(7): 334-353.

[14] [14] ZHANG S, LI J, WU Z, et al. Spatial discontinuity-weighted sparse unmixing of hyperspectral images［J］. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(10): 5767-5779.

[15] [15] HUANG J, HUANG T Z, ZHAO X L, et al. Joint-sparse-blocks regression for total variation regularized hyperspectral unmixing［J］. IEEE Access, 2019, 7(5): 138779-138791.

[16] [16] ZHANG S, LI J, LI H, et al. Spectral-spatial weighted sparse regression for hyperspectral image unmixing［J］. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(6): 3265-3276.

[17] [17] ACHANTA R, SHAJI A, SMITH K, et al. SLIC superpixels compared to state-of-the-art superpixel methods［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11): 2274-2282.

[18] [18] GIAMPOURAS P V, THEMELIS K E, RONTOGIANNIS A A, et al. Simultaneously sparse and low-rank abundance matrix estimation for hyperspectral image unmixing［J］. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(8): 4775-4789.

[19] [19] HUANG J, HUANG T Z, DENG L J, et al. Joint-sparse-blocks and low-rank representation for hyperspectral unmixing［J］. IEEE Transactions on Geoscience and Remote Sensing, 2018, 57(4): 2419-2438.

[20] [20] YANG J, ZHAO Y Q, CHAN J C W, et al. Coupled sparse denoising and unmixing with low-rank constraint for hyperspectral image［J］. IEEE Transactions on Geoscience and Remote Sensing, 2015, 54(3): 1818-1833.

[21] [21] BOYD S, PARIKH N, CHU E, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers［J］. Foundations & Trends in Machine Learning, 2011, 3(1): 1-122.