[1] [1] 闫敬文， 陈宏达， 刘蕾. 高光谱图像分类的研究进展［J］. 光学 精密工程， 2019， 27（3）： 680-693. doi: 10.3788/ope.20192703.0680YANJ W，CHENH D，LIUL. Overview of hyperspectral image classification ［J］. Opt. Precision Eng.， 2019， 27（3）： 680-693. （in Chinese）. doi: 10.3788/ope.20192703.0680

[2] [2] 丁国绅， 乔延利， 易维宁， 等. 基于高光谱图像的改进SIFT特征提取与匹配［J］. 光学 精密工程， 2020， 28（4）： 954-962. doi: 10.3788/OPE.20202804.0954DINGG S，QIAOY L，YIW N，et al. Improved SIFT feature extraction and matching technology based on hyperspectral image ［J］. Opt. Precision Eng.， 2020， 28（4）： 954-962. （in Chinese）. doi: 10.3788/OPE.20202804.0954

[3] [3] 陈静， 张静. 改进高斯过程回归的高光谱空谱联合分类算法［J］. 光学 精密工程， 2019， 27（7）： 1649-1660. doi: 10.3788/ope.20192707.1649CHENJ， ZHANGJ. Spectral-spatial joint classification of hyperspectral image algorithm based on improved Gaussian process regression ［J］. Opt. Precision Eng.， 2019， 27（7）： 1649-1660. （in Chinese）. doi: 10.3788/ope.20192707.1649

[4] Y M CAI, X B LIU, Z H CAI. BS-nets： an end-to-end framework for band selection of hyperspectral image. IEEE Transactions on Geoscience and Remote Sensing, 58, 1969-1984(2020).

[5] F HU, G S XIA, W YANG et al. Mining deep semantic representations for scene classification of high-resolution remote sensing imagery. IEEE Transactions on Big Data, 6, 522-536(2020).

[6] F L LUO, B DU, L P ZHANG et al. Feature learning using spatial-spectral hypergraph discriminant analysis for hyperspectral image. IEEE Transactions on Cybernetics, 49, 2406-2419(2019).

[7] X Y TONG, G S XIA, Q LU et al. Land-cover classification with high-resolution remote sensing images using transferable deep models. Remote Sensing of Environment, 237, 111322(2020).

[8] Y T YU, Y Y LI, J WANG et al. C²-CapsViT： cross-context and cross-scale capsule vision transformers for remote sensing image scene classification. IEEE Geoscience and Remote Sensing Letters, 19, 1-5(2022).

[9] Q ZOU, L H NI, T ZHANG et al. Deep learning based feature selection for remote sensing scene classification. IEEE Geoscience and Remote Sensing Letters, 12, 2321-2325(2015).

[10] Q Bi, K QIN, Z L LI et al. A multiple-instance densely-connected ConvNet for aerial scene classification. IEEE Transactions on Image Processing, 9, 4911-4926(2020).

[11] G CHENG, L M CAI, C B LANG et al. SPNet： Siamese-prototype network for few-shot remote sensing image scene classification. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-11(2022).

[12] E Z LI, J S XIA, P J Du et al. Integrating multilayer features of convolutional neural networks for remote sensing scene classification. IEEE Transactions on Geoscience and Remote Sensing, 55, 5653-5665(2017).

[13] S CHAIB, H LIU, Y F GU et al. Deep feature fusion for VHR remote sensing scene classification. IEEE Transactions on Geoscience and Remote Sensing, 55, 4775-4784(2017).

[14] K J XU, H HUANG, P F DENG et al. Two-stream feature aggregation deep neural network for scene classification of remote sensing images. Information Sciences, 539, 250-268(2020).

[15] J FANG, Y YUAN, X Q LU et al. Robust space–frequency joint representation for remote sensing image scene classification. IEEE Transactions on Geoscience and Remote Sensing, 57, 7492-7502(2019).

[16] X HE, Y S CHEN, Z H LIN. Spatial-spectral transformer for hyperspectral image classification. Remote Sensing, 13, 498(2021).

[17] Y BAZI, L BASHMAL, AL RAHHAL M M et al. Vision transformers for remote sensing image classification. Remote Sensing, 13, 516(2021).

[18] J R ZHANG, H W ZHAO, J LI. TRS： Transformers for remote sensing scene classification. Remote Sensing, 13, 4143(2021).

[19] C SHI, L FANG, Z Y LV et al. Explainable scale distillation for hyperspectral image classification. Pattern Recognition, 122, 108316(2022).

[20] Y T HU, X HUANG, X Y LUO et al. Variational Self-Distillation for Remote Sensing Scene Classification. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-13(2022).

[21] [21] 刘康， 周壮， 李盛阳， 等. 天宫一号高光谱遥感场景分类数据集及应用［J］. 遥感学报， 2020， 24（9）： 1077-1087. doi: 10.11834/jrs.20209323LIUK， ZHOUZ， LIS Y， et al. Scene classification dataset using the Tiangong-1 hyperspectral remote sensing imagery and its applications ［J］. Journal of Remote Sensing， 2020， 24（9）： 1077-1087. （in Chinese）. doi: 10.11834/jrs.20209323

[22] [22] 徐科杰，邓培芳，黄鸿.HSRS-SC：面向遥感场景分类的高光谱图像数据集［J］. 中国图象图形学报，2021，26（8）：1809-1822. doi: 10.11834/jig.200835XUK J， DENGP F， HUANGH. HSRS-SC： a hyperspectral image dataset for remote sensing scene classification ［J］. Journal of Image and Graphics， 2021， 26（8）： 1809-1822. （in Chinese）. doi: 10.11834/jig.200835

[23] Q WANG, W HUANG, Z T XIONG et al. Looking closer at the scene： multiscale representation learning for remote sensing image scene classification. IEEE Transactions on Neural Networks and Learning Systems, 33, 1414-1428(2022).

[24] Q WANG, S T LIU, J CHANUSSOT et al. Scene classification with recurrent attention of VHR remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 57, 1155-1167(2019).