[1] Ye Z, Prasad S, Li W, et al. Classification based on 3D DWT and decision fusion for hyperspectral image analysis[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(1): 173-177.

[2] Liu Dawei, Han Ling, Han Xiaoyong, High spatial resolution remote densing image classification based on deep learning [J]. Acta Optica Sinica, 2016, 36(4): 0428001.

[3] Li Zhimin, Zhang Jie, Huang Hong, et al. Semi-supervised laplace discriminant embedding for hyperspectral image classification[J]. Journal of Electronics & Information Technology, 2015, 37(4): 995-1001.

[4] Li C, Chu H, Kuo B, et al. Hyperspectral image classification using spectral and spatial information based linear discriminant analysis[C]. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2011: 1716-1719.

[5] Tadjudin S, Landgrebe D A. Robust parameter estimation for mixture model[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(1): 439-445.

[6] Sugiyama M. Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis[J]. Journal of Machine Learning Research, 2007, 8(5): 1027-1061.

[7] Lin Yu′e, Li Jingzhao, Liang Xingzhu, et al. Direct orthogonal discriminant locality preserving projections method[J]. Journal of Optoelectronics · Laser, 2012, 23(3): 561-565.

[8] Li W, Prasad S, Fowler J E, et al. Locality-preserving dimensionality reduction and classification for hyperspectral image analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(4): 1185-1198.

[9] Berge A, Solberg A H S. Structured Gaussian components for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(11): 3386-3396.

[10] Ye Z, He M, Fowler J E, et al. Hyperspectral image classification based on spectra derivative features and locality preserving analysis[C]. 2nd IEEE China Summit & International Conference on Signal and Information Processing, 2014: 138-142.

[11] Cai D, He X, Wang X, et al. Locality preserving nonnegative matrix factorization[C]. International Joint Conference on Artificial Intelligence, 2009: 1010-1015.

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

[13] Jia S, Zhu Z, Shen L, et al. A two-stage feature selection framework for hyperspectral image classification using few labeled samples[J]. IEEE Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(4): 1023-1035.

[14] Clausi D A, Jernigan M E. Designing Gabor-filters for optimal texture separability[J]. Pattern Recognition, 2000, 33(11): 1835-1849.

[15] Dundar M M, Landgrebe D. A model-based mixture-supervised classification approach in hyperspectral data analysis[J]. IEEE Transactions on Geoscience Remote Sensing, 2002, 40(12): 2692-2699.

[16] Vlassis N, Likas A. A greedy EM algorithm for Gaussian mixture learning[J]. Neural Processing Letters, 2002, 15(1): 77-87.

[17] Fan Liheng, Lü Junwei, Deng Jiangsheng. Classification of hyperspectral remote sensing images based on bands grouping and classification ensembles [J]. Acta Optica Sinica, 2014, 34(9): 091002.

[18] Wang Shutao, Zeng Qiuju, Song Haobing, et al. Signal denoising method based on the SVM filter absorption methane detection[J]. Chinese J Lasers, 2014, 41(9): 0915001.

[19] Pu H Y, Chen Z, Wang B, et al. A novel spatial-spectral similarity measure for dimensionality reduction and classification of hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(11): 7008-7022.