[1] AHMAD M, SHABBIR S, ROY S K, et al.Hyperspectral image classification-traditional to deep models:a survey for future prospects[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021. [2] RASTI B, HONG Danfeng, HANG Renlong, et al. Feature extraction for hyperspectral imagery:the evolution from shallow to deep:overview and toolbox[J]. IEEE Geoscience and Remote Sensing Magazine, 2020, 8(4):60-88. [3] XUE Zhaohui, NIE Xiangyu. Low-rank and sparse representation with adaptive neighborhood regularization for hyperspectral image classification[J]. Journal of Geodesy and Geoinformation Science, 2022, 5(1):73-90. DOI:10.11947/j.JGGS.2022.0108 [4] 左溪冰,刘冰,余旭初,等.高光谱影像小样本分类的图卷积网络方法[J].测绘学报, 2021, 50(10):1358-1369. DOI:10.11947/j.AGCS.2021.20200155. ZUO Xibing, LIU Bing, YU Xuchu, et al. Graph convolutional network method for small sample classification of hyperspectral images[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(10):1358-1369. DOI:10.11947/j. AGCS.2021.20200155. [5] 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, 2005, 43(3):492-501. [6] CAMPS-VALLS G, BRUZZONE L. Kernel-based methods for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(6):1351-1362. [7] MA Li, CRAWFORD M M, TIAN Jinwei. Local manifold learning-based K-nearest-neighbor for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(11):4099-4109. [8] HU Wei, HUANG Yangyu, WEI Li, et al. Deep convolutional neural networks for hyperspectral image classification[J]. Journal of Sensors, 2015. [9] ZHAO Wenzhi, DU Shihong. Spectral-spatial feature extraction for hyperspectral image classification:a dimension reduction and deep learning approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(8):4544-4554. [10] WANG Kexian, ZHENG Shunyi, LI Rui, et al. A deep double-channel dense network for hyperspectral image classification[J]. Journal of Geodesy and Geoinformation Science, 2021, 4(4):46-62. [11] LIU Xun, DENG Chenwei, CHANUSSOT J, et al. StfNet:a two-stream convolutional neural network for spatiotemporal image fusion[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(9):6552-6564. [12] WU Hao, PRASAD S. Convolutional recurrent neural networks for hyperspectral data classification[J]. Remote Sensing, 2017, 9(3):298. [13] YU Shiqi, JIA Sen, XU Chunyan. Convolutional neural networks for hyperspectral image classification[J]. Neurocomputing, 2017, 219:88-98. [14] GAO Hongmin, YANG Yao, LI Chenming, et al. Joint alternate small convolution and feature reuse for hyperspectral image classification[J]. ISPRS International Journal of Geo-Information, 2018, 7(9):349. [15] RAN Lingyan, ZHANG Yanning, WEI Wei, et al. A hyperspectral image classification framework with spatial pixel pair features[J]. Sensors, 2017, 17(10):2421. [16] ZHONG Zilong, LI Jin, LUO Zhinming, et al. Spectral-spatial residual network for hyperspectral image classification:a 3D deep learning framework[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 56(2):847-858. [17] LI Simin, ZHU Xueyu, LIU Yang, et al. Adaptive spatial-spectral feature learning for hyperspectral image classification[J]. IEEE Access, 2019, 7:61534-61547. [18] ROY S K, MANNA S, SONG Tiecheng, et al. Attention-based adaptive spectral-spatial kernel ResNet for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 59(9):7831-7843. [19] MOU Lichao, LU Xiaoqiang, LI Xuelong, et al. Nonlocal graph convolutional networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(12):8246-8257. [20] 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, 2019, 58(5):3162-3177. [21] WAN Sheng, PAN Shirui, ZHONG Ping, et al. Dual interactive graph convolutional networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60:1-14. [22] HONG Danfeng, GAO Lianru, YAO Jing, et al. Graph convolutional networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 59(7):5966-5978. [23] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[J]. Advances in neural information processing systems, 2017, 30. [24] HAN Kai, WANG Yunhe, CHEN Hanting, et al. A survey on vision transformer[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 45(1):87-110. [25] SUN Hao, ZHENG Xiangtao, LU Xiaoqiang, et al. Spectral-spatial attention network for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 58(5):3232-3245. [26] HONG Danfeng, HAN Zhu, YAO Jing, et al. Spectralformer:rethinking hyperspectral image classification with transformers[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60:1-15. [27] MA Wenping, Yang Qifan, Wu Yue, et al. Double-branch multi-attention mechanism network for hyperspectral image classification[J]. Remote Sensing, 2019, 11(11):1307. [28] WOO S, PARK J, LEE J Y, et al.Cbam:convolutional block attention module[C]//Proceedings of 2018 European conference on computer vision. Switzerland:Springer, 2018:3-19. [29] YANG Kai, SUN Hao, ZOU Chunbo, et al. Cross-attention spectral-spatial network for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60:1-14. [30] LI Rui, ZHENG Shunyi, DUAN C, et al. Classification of hyperspectral image based on double-branch dual-attention mechanism network[J]. Remote Sensing, 2020, 12(3):582. [31] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE conference on computer vision and pattern recognition. Cham:IEEE 2016:770-778. |