[1] Ghamisi P, Yokoya N, Li J, et al. Advances in hyperspectral image and signal processing: a comprehensive overview of the state of the art[J]. Proceedings of the IEEE, 2017, 5(4): 37-78.

    Ghamisi P, Yokoya N, Li J, et al. Advances in hyperspectral image and signal processing: a comprehensive overview of the state of the art[J]. Proceedings of the IEEE, 2017, 5(4): 37-78.

[2] Bioucas-Dias J M, Plaza A, Camps-Valls G, et al. . Hyperspectral remote sensing data analysis and future challenges[J]. Proceedings of the IEEE, 2013, 1(2): 6-36.

    Bioucas-Dias J M, Plaza A, Camps-Valls G, et al. . Hyperspectral remote sensing data analysis and future challenges[J]. Proceedings of the IEEE, 2013, 1(2): 6-36.

[3] Li W, Feng F B, Li H C, et al. Discriminant analysis-based dimension reduction for hyperspectral image classification: a survey of the most recent advances and an experimental comparison of different techniques[J]. Proceedings of the IEEE, 2018, 6(1): 15-34.

    Li W, Feng F B, Li H C, et al. Discriminant analysis-based dimension reduction for hyperspectral image classification: a survey of the most recent advances and an experimental comparison of different techniques[J]. Proceedings of the IEEE, 2018, 6(1): 15-34.

[4] Sun W W, Yang G, Du B, et al. A sparse and low-rank near-isometric linear embedding method for feature extraction in hyperspectral imagery classification[J]. Proceedings of the IEEE, 2017, 55(7): 4032-4046.

    Sun W W, Yang G, Du B, et al. A sparse and low-rank near-isometric linear embedding method for feature extraction in hyperspectral imagery classification[J]. Proceedings of the IEEE, 2017, 55(7): 4032-4046.

[5] 董安国, 李佳逊, 张蓓, 等. 基于谱聚类和稀疏表示的高光谱图像分类算法[J]. 光学学报, 2017, 37(8): 0828005.

    董安国, 李佳逊, 张蓓, 等. 基于谱聚类和稀疏表示的高光谱图像分类算法[J]. 光学学报, 2017, 37(8): 0828005.

    Dong A G, Li J X, Zhang B, et al. Hyperspectral image classification algorithm based on spectral clustering and sparse representation[J]. Acta Optica Sinica, 2017, 37(8): 0828005.

    Dong A G, Li J X, Zhang B, et al. Hyperspectral image classification algorithm based on spectral clustering and sparse representation[J]. Acta Optica Sinica, 2017, 37(8): 0828005.

[6] Zhang W Q, Li X R, Dou Y X, et al. A geometry-based band selection approach for hyperspectral image analysis[J]. Proceedings of the IEEE, 2018, 56(8): 4318-4333.

    Zhang W Q, Li X R, Dou Y X, et al. A geometry-based band selection approach for hyperspectral image analysis[J]. Proceedings of the IEEE, 2018, 56(8): 4318-4333.

[7] Wang M D, Yu J, Niu L J, et al. Feature extraction for hyperspectral images using low-rank representation with neighborhood preserving regularization[J]. Proceedings of the IEEE, 2017, 14(6): 836-840.

    Wang M D, Yu J, Niu L J, et al. Feature extraction for hyperspectral images using low-rank representation with neighborhood preserving regularization[J]. Proceedings of the IEEE, 2017, 14(6): 836-840.

[8] 魏峰, 何明一, 冯燕, 等. 基于矩阵分解的高光谱数据特征提取[J]. 红外与毫米波学报, 2014, 33(6): 674-679.

    魏峰, 何明一, 冯燕, 等. 基于矩阵分解的高光谱数据特征提取[J]. 红外与毫米波学报, 2014, 33(6): 674-679.

    Wei F, He M Y, Feng Y, et al. Feature extraction on matrix factorization for hyperspectral data[J]. Journal of Infrared and Millimeter Waves, 2014, 33(6): 674-679.

    Wei F, He M Y, Feng Y, et al. Feature extraction on matrix factorization for hyperspectral data[J]. Journal of Infrared and Millimeter Waves, 2014, 33(6): 674-679.

[9] Licciardi G, Marpu P R, Chanussot J, et al. Linear versus nonlinear PCA for the classification of hyperspectral data based on the extended morphological profiles[J]. Proceedings of the IEEE, 2012, 9(3): 447-451.

    Licciardi G, Marpu P R, Chanussot J, et al. Linear versus nonlinear PCA for the classification of hyperspectral data based on the extended morphological profiles[J]. Proceedings of the IEEE, 2012, 9(3): 447-451.

[10] Green A A, Berman M, Switzer P, et al. A transformation for ordering multispectral data in terms of image quality with implications for noise removal[J]. Proceedings of the IEEE, 1988, 26(1): 65-74.

    Green A A, Berman M, Switzer P, et al. A transformation for ordering multispectral data in terms of image quality with implications for noise removal[J]. Proceedings of the IEEE, 1988, 26(1): 65-74.

[11] Crawford M, Held A A, Burger J. Nonlinear manifolds for feature extraction: opportunities and challenge[J]. Proceedings of the IEEE, 2011: 1-3.

    Crawford M, Held A A, Burger J. Nonlinear manifolds for feature extraction: opportunities and challenge[J]. Proceedings of the IEEE, 2011: 1-3.

[12] Lunga D, Prasad S, Crawford M M, et al. Manifold-learning-based feature extraction for classification of hyperspectral data: a review of advances in manifold learning[J]. Proceedings of the IEEE, 2014, 31(1): 55-66.

    Lunga D, Prasad S, Crawford M M, et al. Manifold-learning-based feature extraction for classification of hyperspectral data: a review of advances in manifold learning[J]. Proceedings of the IEEE, 2014, 31(1): 55-66.

[13] Ma L, Crawford M M, Yang X Q, et al. Local-manifold-learning-based graph construction for semisupervised hyperspectral image classification[J]. Proceedings of the IEEE, 2015, 53(5): 2832-2844.

    Ma L, Crawford M M, Yang X Q, et al. Local-manifold-learning-based graph construction for semisupervised hyperspectral image classification[J]. Proceedings of the IEEE, 2015, 53(5): 2832-2844.

[14] Zhai Y G, Zhang L F, Wang N, et al. A modified locality-preserving projection approach for hyperspectral image classification[J]. Proceedings of the IEEE, 2016, 13(8): 1059-1063.

    Zhai Y G, Zhang L F, Wang N, et al. A modified locality-preserving projection approach for hyperspectral image classification[J]. Proceedings of the IEEE, 2016, 13(8): 1059-1063.

[15] 陈新忠, 胡汇涓, 王雪松. 基于加权近邻保持嵌入的高光谱数据降维方法[J]. 中国矿业大学学报, 2013, 42(6): 1066-1072.

    陈新忠, 胡汇涓, 王雪松. 基于加权近邻保持嵌入的高光谱数据降维方法[J]. 中国矿业大学学报, 2013, 42(6): 1066-1072.

    Chen X Z, Hu H J, Wang X S. Dimensionality reduction for hyperspectral data using weighted neighborhood preserving embedding[J]. Journal of China University of Mining & Technology, 2013, 42(6): 1066-1072.

    Chen X Z, Hu H J, Wang X S. Dimensionality reduction for hyperspectral data using weighted neighborhood preserving embedding[J]. Journal of China University of Mining & Technology, 2013, 42(6): 1066-1072.

[16] He XF, NiyogiP. Locality preserving projection[C]. Advances in Neural Information Processing System, 2004( 16): 153- 160.

    He XF, NiyogiP. Locality preserving projection[C]. Advances in Neural Information Processing System, 2004( 16): 153- 160.

[17] Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000, 290(5500): 2323-2326.

    Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000, 290(5500): 2323-2326.

[18] 陈昭, 王斌, 张立明. 基于低秩张量分析的高光谱图像降维与分类[J]. 红外与毫米波学报, 2013, 32(6): 569-575.

    陈昭, 王斌, 张立明. 基于低秩张量分析的高光谱图像降维与分类[J]. 红外与毫米波学报, 2013, 32(6): 569-575.

    Chen Z, Wang B, Zhang L M. Dimensionality reduction and classification based on lower rank tensor analysis for hyperspectral imagery[J]. Journal of Infrared and Millimeter Waves, 2013, 32(6): 569-575.

    Chen Z, Wang B, Zhang L M. Dimensionality reduction and classification based on lower rank tensor analysis for hyperspectral imagery[J]. Journal of Infrared and Millimeter Waves, 2013, 32(6): 569-575.

[19] An J L, Zhang X R, Zhou H Y, et al. Tensor-based low-rank graph with multimanifold regularization for dimensionality reduction of hyperspectral images[J]. Proceedings of the IEEE, 2018, 56(8): 4731-4746.

    An J L, Zhang X R, Zhou H Y, et al. Tensor-based low-rank graph with multimanifold regularization for dimensionality reduction of hyperspectral images[J]. Proceedings of the IEEE, 2018, 56(8): 4731-4746.

[20] Li W, Du Q. Collaborative representation for hyperspectral anomaly detection[J]. Proceedings of the IEEE, 2015, 53(3): 1463-1474.

    Li W, Du Q. Collaborative representation for hyperspectral anomaly detection[J]. Proceedings of the IEEE, 2015, 53(3): 1463-1474.

[21] Sidiropoulos N D, de Lathauwer L, Fu X, et al. . Tensor decomposition for signal processing and machine learning[J]. Proceedings of the IEEE, 2017, 65(13): 3551-3582.

    Sidiropoulos N D, de Lathauwer L, Fu X, et al. . Tensor decomposition for signal processing and machine learning[J]. Proceedings of the IEEE, 2017, 65(13): 3551-3582.

[22] Deng Y J, Li H C, Fu K, et al. Tensor low-rank discriminant embedding for hyperspectral image dimensionality reduction[J]. Proceedings of the IEEE, 2018, 56(12): 7183-7194.

    Deng Y J, Li H C, Fu K, et al. Tensor low-rank discriminant embedding for hyperspectral image dimensionality reduction[J]. Proceedings of the IEEE, 2018, 56(12): 7183-7194.

[23] Makantasis K, Doulamis A D, Doulamis N D, et al. Tensor-based classification models for hyperspectral data analysis[J]. Proceedings of the IEEE, 2018, 56(12): 6884-6898.

    Makantasis K, Doulamis A D, Doulamis N D, et al. Tensor-based classification models for hyperspectral data analysis[J]. Proceedings of the IEEE, 2018, 56(12): 6884-6898.

[24] Zhang T H, Tao D C, Li X L, et al. Patch alignment for dimensionality reduction[J]. Proceedings of the IEEE, 2009, 21(9): 1299-1313.

    Zhang T H, Tao D C, Li X L, et al. Patch alignment for dimensionality reduction[J]. Proceedings of the IEEE, 2009, 21(9): 1299-1313.