[1] Matsuki T, Yokoya N, Iwasaki A. Hyperspectral tree species classification of Japanese complex mixed forest with the aid of lidar data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 2177-2187(2015).
[2] Liu Y Z, Zhu Z Z, Ma F. Review of hyperspectral image classification based on feature fusion method[J]. Laser & Optoelectronics Progress, 58, 0400004(2021).
[3] Zhang M M, Li W, Du Q. Diverse region-based CNN for hyperspectral image classification[J]. IEEE Transactions on Image Processing, 27, 2623-2634(2018).
[4] Zhang X D, Wang T J, Zhu S J et al. Hyperspectral image classification based on dilated convolutional attention neural network[J]. Acta Optica Sinica, 41, 0310001(2021).
[5] Guo Y H, Yin X J, Zhao X C et al. Hyperspectral image classification with SVM and guided filter[J]. EURASIP Journal on Wireless Communications and Networking, 2019, 56(2019).
[6] Feng F, Wang S T, Zhang J et al. Hyperspectral images classification based on multi-feature fusion and hybrid convolutional neural networks[J]. Laser & Optoelectronics Progress, 58, 0810010(2021).
[7] Liu J X, Ban W, Chen Y et al. Multi-dimensional CNN fused algorithm for hyperspectral remote sensing image classification[J]. Chinese Journal of Lasers, 48, 1610003(2021).
[8] Chen Y S, Jiang H L, Li C Y et al. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 54, 6232-6251(2016).
[9] Zhao C H, Zhu W X, Feng S. Hyperspectral image classification based on kernel-guided deformable convolution and double-window joint bilateral filter[J]. IEEE Geoscience and Remote Sensing Letters, 19, 5506505(2022).
[10] Kipf T N, Max W. Semi-supervised classification with graph convolutional networks[C](2017).
[11] Guo Q, Peng L. Hyperspectral classification based on 3D convolutional neural network and super pixel segmentation[J]. Acta Optica Sinica, 41, 2210001(2021).
[12] Shahraki F F, Prasad S. Graph convolutional neural networks for hyperspectral data classification[C], 968-972(2018).
[13] Zhang M H, Luo H L, Song W et al. Spectral-spatial offset graph convolutional networks for hyperspectral image classification[J]. Remote Sensing, 13, 4342(2021).
[14] Hong D F, Gao L R, Yao J et al. Graph convolutional networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 59, 5966-5978(2021).
[15] Danel T, Spurek P, Tabor J et al. Spatial graph convolutional networks[M]. Yang H Q, Pasupa K, Leung A C S, et al. Neural information processing, 1333, 668-675(2020).
[16] Qin A Y, Shang Z W, Tian J Y et al. Spectral-spatial graph convolutional networks for semisupervised hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 16, 241-245(2019).
[17] Wan S, Gong C, Zhong P et al. Hyperspectral image classification with context-aware dynamic graph convolutional network[J]. IEEE Transactions on Geoscience and Remote Sensing, 59, 597-612(2021).
[18] Bai J, Ding B X, Xiao Z et al. Hyperspectral image classification based on deep attention graph convolutional network[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 5504316(2022).
[19] Mu C H, Liu Y J, Liu Y. Hyperspectral image spectral-spatial classification method based on deep adaptive feature fusion[J]. Remote Sensing, 13, 746(2021).
[20] Defferrard M, Bresson X, Vandergheynst P. Convolutional neural networks on graphs with fast localized spectral filtering[C], 3837-3845(2016).
[21] Hammond D K, Vandergheynst P, Gribonval R. Wavelets on graphs via spectral graph theory[J]. Applied and Computational Harmonic Analysis, 30, 129-150(2011).