[1] Landgrebe, D. Hyperspectral image data analysis[J]. IEEE Signal Processing Magazine,2002,19(1):17—28.
[2] Sabbah S, Harig R, Rusch P, et al. Remote sensing of gases by hyperspectral imaging: System performance and measurements[J]. Optical Engineering,2012,51(11):111717.
[3] Krupnik D, Khan S. Close-range, ground-based hyperspectral imaging for mining applications at various scales: Review and case studies[J]. Earth-Science Reviews,2019,198:102952.
[4] Fan J, Zhou N, Peng J, et al. Hierarchical learning of tree classifiers for large-scale plant species identification[J]. IEEE Transactions on Image Processing,2015,24(11):4172—4184.
[5] Shimoni M, Haelterman R, Perneel C. Hyperspectral imaging for military and security applications: Combining myriad processing and sensing techniques[J]. IEEE Geoscience and Remote Sensing Magazine,2019,7(2):101—117.
[6] Zhang L, Zhang L, Du B. Deep learning for remote sensing data: A technical tutorial on the state of the art[J]. IEEE Geoscience and Remote Sensing Magazine,2016,4(2):22—40.
[7] Boulila W, Sellami M, Driss M, et al. Ghaleb F A. RS-DCNN: A novel distributed convolutional neural networks-based approach for big remote sensing image classification[J]. Computers and Electronics in Agriculture,2021,182:106014.
[8] Paoletti M E, Haut J M, Fernandez-Beltran R, et al. Deep pyramidal residual networks for spectral–spatial hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing,2018,57(2):740—754.
[9] Hu W, Huang Y, Wei L, et al. Deep convolutional neural networks for hyperspectral image classification[J]. Journal of Sensors,2015,2015:1—12.
[10] Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks[EB/OL]. (2016-09-09)/[2024-09-24]. https:∥arxiv.org/abs/1609.02907
[11] Qin A, Shang Z, Tian J, et al. Spectral-spatial graph convolutional networks for semi-supervised hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters,2018,16(2):241—245.
[12] 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,2020,59(1):597—612.
[13] Hong D, Gao L, Yao J, et al. Graph convolutional networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing,2020,59(7):5966—5978.
[14] Liu Q, Xiao L, Yang J, et al. CNN-enhanced graph convolutional network with pixel- and superpixel-level feature fusion for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing,2020,59(10):8657—8671.
[15] Achanta R, Shaji A, Smith K, et al. SLIC superpixels compared to state-of-the-art superpixel methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(11):2274—2282.
[16] Dong Y, Liu Q, Du B, et al. Weighted feature fusion of convolutional neural network and graph attention network for hyperspectral image classification[J]. IEEE Transactions on Image Processing,2022,31:1559—1572.
[17] Xanthopoulos P, Pardalos P M, Trafalis T B. Linear discriminant analysis[A]. Robust Data Mining,2013:27—33.
[18] Jampani V, Sun D, Liu M-Y, et al. Superpixel sampling networks[C]∥Proceedings of the European Conference on Computer Vision(ECCV). Munich, Germany:Springer,2018:352—368.
[19] He N, Fang L, Plaza A. Hybrid first and second order attention U-Net for building segmentation in remote sensing images[J]. Science China Information Sciences,2020,63:1—12.
[20] Fu J, Liu J, Tian H, et al. Dual attention network for scene segmentationCCC∥Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA:IEEE,2019:3146—3154
[21] Chen L-C, Papandreou G, Kokkinos I, et al. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,40(4):834—848.
[22] Sun K, Xiao B, Liu D, et al. Deep high-resolution representation learning for human pose estimation[C]∥Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE,2019:5693—5703.
[23] Woo S, Park J, Lee J-Y, et al. CBAM: Convolutional block attention module[C]∥Proceedings of the European Conference on Computer Vision(ECCV). Munich,Germany:Springer,2018:3—19.
[24] Chen Y, Jiang H, Li C, et al. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing,2016,54(10):6232—6251.
[25] Wang Z, Du B, Guo Y. Domain adaptation with neural embedding matching[J]. IEEE Transactions on Neural Networks and Learning Systems,2019,31(7):2387—2397.
[26] Qu L, Zhu X, Zheng J, et al. Triple-attention-based parallel network for hyperspectral image classification[J]. Remote Sensing,2021,13(2):324.
[27] Zhong Z, Li J, Luo Z, Chapman M. Spectral–spatial residual network for hyperspectral image classification: A 3-D deep learning framework[J]. IEEE Transactions on Geoscience and Remote Sensing,2017,56(2):847—858.
[28] Roy S K, Manna S, Song T, 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.
[29] Zhou Y, Huang X, Yang X, et al. DCTN: Dual-branch convolutional transformer network with efficient interactive self-attention for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing,2024,62(5):1234—1245.