[1] Du P J, Xia J S, Xue Z H et al. Review of hyperspectral remote sensing image classification[J]. Journal of Remote Sensing, 20, 236-256(2016).

[2] Wang X F, Zhang J P, Yan Q J et al. Target detection for hyperspectral image based on support vector data description[J]. Chinese Journal of Lasers, 41, s114003(2014).

[3] Liu L X, Li M Z, Zhao Z G et al. Recent advances of hyperspectral imaging application in biomedicine[J]. Chinese Journal of Lasers, 45, 0207017(2018).

[4] Huang H, Wang L H, Shi G Y. Spatially-regularized manifold discriminant analysis algorithm for hyperspectral image classification[J]. Acta Optica Sinica, 40, 0228001(2020).

[5] Luo F L, Du B, Zhang L P et al. Feature learning using spatial-spectral hypergraph discriminant analysis for hyperspectral image[J]. IEEE Transactions on Cybernetics, 49, 2406-2419(2019). http://ieeexplore.ieee.org/document/8351966

[6] Du B, Huang Z Q, Wang N et al. Joint weighted nuclear norm and total variation regularization for hyperspectral image denoising[J]. International Journal of Remote Sensing, 66, 1646-1657(2017).

[7] Bi X J, Zhou Z Y. Hyperspectral image classification algorithm based on two-channel generative adversarial network[J]. Acta Optica Sinica, 39, 1028002(2019).

[8] Feng F B, Li W, Du Q et al. Dimensionality reduction of hyperspectral image with graph-based discriminant analysis considering spectral similarity[J]. Remote Sensing, 9, 323-327(2017). http://adsabs.harvard.edu/abs/2017RemS....9..323F

[9] Fang L Y, Wang C, Li S T et al. Hyperspectral image classification via multiple-feature-based adaptive sparse representation[J]. IEEE Transactions on Instrumentation and Measurement, 66, 1646-1657(2017). http://ieeexplore.ieee.org/document/7880591

[10] Ji L, Zhang X, Zhang L M et al. Hyperspectral image classification algorithm based on space-spectral weighted nearest neighbor[J]. Laser & Optoelectronics Progress, 57, 061013(2020).

[11] Huang H, Zheng X L. Hyperspectral image classification with combination of weighted spatial-spectral and KNN[J]. Optics and Precision Engineering, 24, 873-881(2016).

[12] Awad M M. Forest mapping: a comparison between hyperspectral and multispectral images and technologies[J]. Journal of Forestry Research, 29, 1395-1405(2018).

[13] Wang K, Cheng L G, Yong B. Spectral-similarity-based kernel of SVM for hyperspectral image classification[J]. Remote Sensing, 12, 2154(2020).

[14] Huang H, Duan Y L, Shi G Y et al. Fusion of weighted mean reconstruction and SVMCK for hyperspectral image classification[J]. IEEE Access, 6, 15224-15235(2018). http://ieeexplore.ieee.org/document/8279652/citations

[15] Chen Z K, Jiang J J, Zhou C et al. SuperBF: superpixel-based bilateral filtering algorithm and its application in feature extraction of hyperspectral images[J]. IEEE Access, 7, 147796-147807(2019). http://ieeexplore.ieee.org/document/8819879

[16] Tu B, Zhang X F, Kang X D et al. Hyperspectral image classification via fusing correlation coefficient and joint sparse representation[J]. IEEE Geoscience and Remote Sensing Letters, 15, 340-344(2018).

[17] Cui M S, Prasad S. Class-dependent sparse representation classifier for robust hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 53, 2683-2695(2015). http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6957565&punumber=36

[18] Ye Z, Dong R, Bai L et al. Hyperspectral image classification based on segmented local binary patterns[J]. Sensing and Imaging, 21, 1-16(2020). http://link.springer.com/article/10.1007/s11220-020-0274-7