[1] [1] Camps-Valls G, Tuia D, Bruzzone L, et al. Advances in hyperspectral image classification［J］. IEEE Signal Processing Magazine, 2014, 31(1): 45-54.

[2] [2] Li Tie, Sun Jinguang, Zhang Xinjun, et al. Research of hyperspectral image classification based on hierarchical sparse representation feature learning［J］. Laser & Optoelectronics Progress, 2016, 53(9): 091001.

[3] [3] Tong Q, Xue Y, Zhang L. Progress in hyperspectral remote sensing science and technology in China over the past three decades［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(1): 70-91.

[4] [4] Li Y, Xie W, Li H. Hyperspectral image reconstruction by deep convolutional neural network for classification［J］. Pattern Recognition, 2017, 63: 371-383.

[5] [5] Deng S, Xu Y, He Y, et al. A hyperspectral image classification framework and its application［J］. Information Sciences, 2015, 299: 379-393.

[6] [6] Braun A C, Weidner U, Hinz S. Support vector machines, import vector machines and relevance vector machines for hyperspectral classification-A comparison［C］. 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2011: 12386162.

[7] [7] Huang Hong, Zheng Xinlei. Hyperspectral image classification with combination of weighted spatial-spectral and and KNN［J］. Optics and Precision Engineering, 2016, 24(4): 873-881.

[8] [8] Haq Q S U, Shi L, Tao L, et al. A I-1-minimization based approach for hyperspectral data classification［C］. International Conference on Advanced Materials in Microwaves and Optics, 2012, 500: 675-681.

[9] [9] Chen Y, Nasrabadi N M, Tran T D. Hyperspectral image classification using dictionary-based sparse representation［J］. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(10): 3973-3985.

[10] [10] Wang Jianing. Hyperspectral image classification based on joint sparse representation and morphological feature extraction［J］. Laser & Optoelectronics Progress, 2016, 53(8): 082801.

[11] [11] Chen Shanxue, Gui Chengming, Wang Yining. Close coupled set of pixels-based adaptive boosting class-wise sparse representation classifier for robust hyperspectral image classification［J］. Systems Engineering and Electronics, 2017, 39(3): 655-661.

[12] [12] Fang L, Li S, Kang X, et al. Spectral-spatial hyperspectral image classification via multiscale adaptive sparse representation［J］. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(12): 7738-7749.

[13] [13] Fang L, Li S, Kang X, et al. Spectral-spatial classification of hyperspectral images with a superpixel-based discriminative sparse model［J］. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(8): 4186-4201.

[14] [14] Zhang H, Li J, Huang Y, et al. A nonlocal weighted joint sparse representation classification method for hyperspectral imagery［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6): 2056-2065.

[15] [15] Song Lin, Chen Yongmei, Zhao Yongqiang. Hyper-spectrum classification based on sparse representation model and auto-regressive model［J］. Acta Optica Sinica, 2012, 32(3): 0330003.

[16] [16] Du P, Xue Z, Li J, et al. Learning discriminative sparse representations for hyperspectral image classification［J］. IEEE Journal of Selected Topics in Signal Processing, 2015, 9(6): 1089-1104.