[2] TONG Qingxi, ZHANG Bing, ZHANG Lifu. Current progress of hyperspectral remote sensing in China[J]. Journal of Remote Sensing, 20, 689-707(2016).

[3] DU Peijun, XIA Junshi, XUE Zhaohui et al. Review of hyperspectral remote sensing image classification[J]. Journal of Remote Sensing, 20, 236-256(2016).

[4] ADDABBO P, FISCANTE N, GIUNTA G et al. Multiple sub-pixel target detection for hyperspectral imaging systems[J]. IEEE Transactions on Signal Processing, 71, 1599-1611(2023).

[5] TRANSON J, D'ANDRIMONT R, MAUGNARD A et al. Survey of hyperspectral earth observation applications from space in the sentinel-2 context[J]. Remote Sensing, 10, 157(2018).

[6] ZHANG Yiwei, GUO Yanpei, TANG Rong et al. Progress and trends of application of hyperspectral remote sensing in plant diversity research[J]. National Remote Sensing Bulletin, 27, 2467-2483(2023).

[7] ZHONG Yanfei, WANG Xinyu, HU Xin et al. Hyperspectral with high-spatial resolution remote sensing from observation, processing to applications[J]. Acta Geodaetica et Cartographica Sinica, 52, 1212-1226(2023).

[8] PAOLETTI M E, HAUT J M, PLAZA J et al. Deep learning classifiers for hyperspectral imaging: a review[J]. Journal of Photogrammetry and Remote Sensing, 158, 279-317(2019).

[9] QIAO Yulong, PAN Jengshyang, SUN Shenghe. Improved K nearest neighbors classification algorithm[J]. Acta Electronica Sinica, 33, 1146-1149(2005).

[10] SCOTT C, NOWAK R D. Minimax-optimal classification with dyadic decision trees[J]. IEEE Transactions on Information Theory, 52, 1335-1353(2006).

[11] HAM J, CHEN Yangchi, CRAWFORD M M et al. Investigation of the random forest framework for classification of hyperspectral data[J]. IEEE Transactions on Geoscience and Remote Sensing, 43, 492-501(2005).

[12] GUO Baofeng, GUNN S R, DAMPER R I et al. Customizing kernel functions for SVM-based hyperspectral image classification[J]. IEEE Transactions on Image Processing, 17, 622-629(2008).

[13] TARABALKA Y, BENEDIKTSSON J A, CHANUSSOT J. Spectral-spatial classification of hyperspectral imagery based on partitional clustering techniques[J]. IEEE Transactions on Geoscience and Remote Sensing, 47, 2973-2987(2009).

[14] BRO R, SMILDE A K. Principal component analysis. Analytical Methods, 6, 2812-2831(2014).

[15] GEPSHTEIN S, KELLER Y. Iterative spectral independent component analysis[J]. Signal Processing, 155, 368-376(2019).

[16] CHEN Guangyi, KRZYŻAK A, QIAN Shenen. Noise robust hyperspectral image classification with MNF-based edge preserving features[J]. Image Analysis & Stereology, 42, 93-99(2023).

[17] BENEDIKTSSON J A, PALMASON J A, SVEINSSON J R. Classification of hyperspectral data from urban areas based on extended morphological profiles[J]. IEEE Transactions on Geoscience Remote Sensing, 43, 480-491(2005).

[18] BEIRAMI B A, MOKHTARZADE M. Band grouping SuperPCA for feature extraction and extended morphological profile production from hyperspectral images[J]. IEEE Geoscience and Remote Sensing Letters, 17, 1953-1957(2020).

[19] LIU Xiaobo, YIN Xu, CAI Yaoming et al. Visual saliency-based extended morphological profiles for unsupervised feature learning of hyperspectral images[J]. IEEE Geoscience and Remote Sensing Letters, 17, 1963-1967(2020).

[20] CHEN Yushi, LIN Zhouhan, ZHAO Xing et al. Deep learning-based classification of hyperspectral data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 2094-2107(2014).

[21] CHENG Rongjie, YANG Yun, LI Longwei et al. Lightweight residual network based on depthwise separable convolution for hyperspectral image classification[J]. Acta Optica Sinica, 43, 1228010(2023).

[22] MOU Lichao, GHAMISI P, ZHU Xiaoxiang. Deep recurrent neural networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience & Remote Sensing, 55, 3639-3655(2017).

[23] SUN Caihao, ZHANG Xiaohua, MENG Hongyun et al. AC-WGAN-GP: generating labeled samples for improving hyperspectral image classification with small-samples[J]. Remote Sensing, 14, 4910(2022).

[24] ZHOU Yu, ZHENG Haixia, HUANG Xin et al. Graph neural networks: taxonomy, advances, and trends[J]. ACM Transactions on Intelligent Systems and Technology, 13, 59(2022).

[25] WU Guoyong, AL-QANESS M A A, Al-ALIMI D et al. Hyperspectral image classification using graph convolutional network: A comprehensive review[J]. Expert Systems with Applications, 257, 125106(2024).

[26] LIANG Guojun, U K, YIN Haichang et al. Multi-scale hybrid attention graph convolution neural network for remote sensing images super-resolution[J]. Signal Processing, 207, 108954(2023).

[27] QIN Anyong, SHANG Zhaowei, TIAN Jinyu et al. Spectral-spatial graph convolutional networks for semisupervised hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 16, 241-245(2019).

[28] HE Xin, CHEN Yushi, GHAMISI P. Dual graph convolutional network for hyperspectral image classification with limited training samples[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-18(2022).

[29] HONG Danfeng, GAO Lianru, YAO Jing et al. Graph convolutional networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 59, 5966-5978(2021).

[30] WAN Sheng, GONG Chen, ZHONG Ping et al. Multiscale dynamic graph convolutional network for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 58, 3162-3177(2020).

[31] ZHOU Hao, LUO Fulin, ZHUANG Huiping et al. Attention multihop graph and multiscale convolutional fusion network for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-14(2023).

[32] SHA Anshu, WANG Bin, WU Xiaofeng et al. Semisupervised classification for hyperspectral images using graph attention networks[J]. IEEE Geoscience and Remote Sensing Letters, 18, 157-161(2021).

[33] YANG Chunlan, XUE Dawei. EGCN: enhanced graph convolutional network for hyperspectral image classification[C](2024).

[34] WANG Xiaolong, GIRSHICK R, GUPTA A et al. Non-local neural networks[C], 7794-7803(2018).

[35] VASWANI A, SHAZEER N, PARMAR N et al. Attention is all you need[C], 6000-6010(2017).

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

[37] HAN Kai, WANG Yunhe, CHEN Hanting et al. A survey on vision transformer[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 87-110(2023).

[38] HE Ji, ZHAO Lina, YANG Hongwei et al. HSI-BERT: hyperspectral image classification using the bidirectional encoder representation from transformers[J]. IEEE Transactions on Geoscience and Remote Sensing, 58, 165-178(2020).

[39] HONG Danfeng, HAN Zhu, YAO Jing et al. SpectralFormer: rethinking hyperspectral image classification with transformers[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-15(2022).

[40] ZHONG Zilong, LI Ying, MA Lingfei et al. Spectral-spatial transformer network for hyperspectral image classification: a factorized architecture search framework[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-15(2022).

[41] YANG Xiaofei, CAO Weijia, LU Yao et al. Hyperspectral image transformer classification networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-15(2022).

[42] SUN Le, ZHAO Guangrui, ZHENG Yuhui et al. Spectral-spatial feature tokenization transformer for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-14(2022).

[43] HE Xin, CHEN Yushi, LIN Zhouhan. Spatial-spectral transformer for hyperspectral image classification[J]. Remote Sensing, 13, 498(2021).

[44] ROY S K, DERIA A, SHAH C et al. Spectral-spatial morphological attention transformer for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-15(2023).

[45] ROY S K, DERIA A, HONG Danfeng et al. Multimodal fusion transformer for remote sensing image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-20(2023).

[46] HAMIDA ABEN, BENOIT A, LAMBERT P et al. 3-D deep learning approach for remote sensing image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 56, 4420-4434(2018).

[47] ROY S K, KRISHNA G, DUBEY S R et al. HybridSN: exploring 3D-2D CNN feature hierarchy for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 17, 277-281(2020).

[48] OUYANG E, LI Bin, HU Wenjing et al. When multigranularity meets spatial-spectral attention: a hybrid transformer for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-18(2023).

[49] ZHAO Ziqi, YANG Changbao, QIU Zhongjun et al. Discrete cosine transform-based joint spectral-spatial information compression and band-correlation calculation for hyperspectral feature extraction[J]. Remote Sensing, 16, 4270(2024).