[1] FAUVEL M, TARABALKA Y, BENEDIKTSSON A, et al. Advances in spectral-spatial classification of hyperspectral images［J］. Proceedings of the IEEE, 2013, 101(3): 652-675.

[2] HANG Hong, YANG Ya-qiong, LUO Fu-lin, et al. Classification of hyperspectral images based on semi-supervised sparse multi-manifold embedding［J］. Acta Photonica Sinica, 2016, 45(3): 0330001.

[3] HANG Hong, ZHENG Xin-lei, LUO Fu-lin, et al. Hyperspectral image classification with combination of weighted mean filter and manifold reconstruction preserving embedding［J］. Acta Photonica Sinica, 2016, 45(10): 1030001.

[4] HANG Hong, ZHENG Xin-lei, Hyperspectral image classification with combination of weighted spatial–spectral and KNN［J］. Optics and Precision Engineering,2016, 24(4): 873-880.

[5] WANGZeng-mao, DU Bo, ZHANG Liang-pei, et al. Based on texture feature and extend morphological profile fusion for hyperspectral image classification ［J］. Acta Photonica Sinica, 2014, 43(8): 0810002.

[6] Pattern Classification［M］, DUDA.R.O, HART .P. E, STOCK. D. G., 2nd ed.New York, NY, USA: Wiley, 2001.

[7] HE X F, CAI D, NIYOGI P. Laplacian score for feature selection ［C］. Proceedings of Advance in Neural Information Processing Systems, 2005, 507-514.

[8] ZHAO Z, LIU H. Spectral feature selection for supervised and unsupervised learning［C］. ICML, 2007: 1151-1157.

[9] ZHAO Z, WANG L, LIU H. Efficient spectral feature selection with minimum redundancy［C］. AAAI, 2010: 673-678.

[10] NIE F P, HUANG H, CAI X, et al. Efficient and robust feature selection via joint l2,1-norms minimization［C］. Proceedings of Advances in Neural Information Processing System, 2010: 1813-1821.

[11] YANG Y, SHEN H, MA Z, et al.L21-norm regularized discriminative feature selection for unsupervised learning［C］. In Proceedings of the 22th IJCAI, 2011: 1589-1594.

[12] LIU X W, WANG L, ZHANG J, et al. Global and local structure preservation for feature selection［J］. IEEE Transactions on Neural Network and Learning System, 2013, 53(2): 982-993.

[13] CAMPS-VALLS G, GOMEZ-CHOVA L, MUNOZ-MARI J, et al. Composite kernels for hyperspectral image classification［J］. IEEE Geoscience and Remote Sensing Letter,2006, 3(1): 93-97.

[14] ZHOU Y C, PENG J T, CHEN C L P, Dimension reduction using spatial and spectral regularized local discriminant embedding for hyperspectral image classification［J］. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(2): 1082-1095.

[15] KANG X, LI S, BENEDIKTSSON J A. Spectral-spatial hyperspectral image classification with edge-preserving filtering［J］. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(2): 2666-2677.

[16] HADOUX X, JAY S, RABATEL G, et al.A spectral-spatial approach for hyperspectral image classification using spatial regularization on supervised score image［J］.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(6): 2361-2369.

[17] XIA J, BOMBRUN L, ADALI Ti, et al.Spectral-spatial classification of hyperspectral data using ICA and edge preserving filtering via an ensemble strategy［J］.IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(8): 4971-4981.

[18] KANG X, LI S, BENEDIKTSSON J A. Feature extraction of hyperspectral images with image fusion and recursive filtering［J］ IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(6): 3742-3752.

[19] CHEN Y S, LIN Z H, ZHAO X, et al. Deep learning based classification of hyperspectral data［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6): 2094-2107.

[20] ZHOU Y C, WEI Y. Learning hierarchical spectral-spatial features for hyperspectral image classification［J］. IEEE Transactions on Cybernetics, 2016, 56(7): 1667-1678.

[21] ROWEIS S, SAUL L. Nonlinear dimensionality reduction by locally linear embedding［J］. Science, 2000, 290(5500): 2323-2326.