[1] Wang A L, Wang Y, Chen Y S. Hyperspectral image classification based on convolutional neural network and random forest[J]. Remote Sensing Letters, 10, 1086-1094(2019).
[2] Bo C J, Lu H C, Wang D. Spectral-spatial K-Nearest neighbor approach for hyperspectral image classification[J]. Multimedia Tools and Applications, 77, 10419-10436(2018).
[3] Zhao C H, Qi B, Zhang Y. Hyperspectral image classification based on variational relevance vector machine[J]. Acta Optica Sinica, 32, 0828004(2012).
[4] 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).
[5] Ye Z, Bai L. Hyperspectral image classification based on principal component analysis and local binary patterns[J]. Laser & Optoelectronics Progress, 54, 111006(2017).
[6] Zaatour R, Bouzidi S, Zagrouba E. Independent component analysis-based band selection techniques for hyperspectral images analysis[J]. Journal of Applied Remote Sensing, 11, 026006(2017).
[7] Ma S X, Liu C T, Li H C et al. Feature extraction based on linear embedding and tensor manifold for hyperspectral image[J]. Acta Optica Sinica, 39, 0412001(2019).
[8] Wei L F, Yu M, Zhong Y F et al. Hyperspectral image classification method based on space-spectral fusion conditional random field[J]. Acta Geodaetica et Cartographica Sinica, 49, 343-354(2020).
[9] Hu X, Lu Q K. Hyperspectral image classification algorithm based on saliency profile[J]. Acta Optica Sinica, 40, 1611001(2020).
[10] Fauvel M, Benediktsson J A, Chanussot J et al. Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles[J]. IEEE Transactions on Geoscience and Remote Sensing, 46, 3804-3814(2008).
[11] 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 and Remote Sensing, 43, 480-491(2005).
[12] Hu W, Huang Y Y, Wei L et al. Deep convolutional neural networks for hyperspectral image classification[J]. Journal of Sensors, 2015, 258619(2015).
[13] Makantasis K, Karantzalos K, Doulamis A et al. Deep supervised learning for hyperspectral data classification through convolutional neural networks[C], 4959-4962(2015).
[14] Hu L, Shan R, Wang F et al. Hyperspectral image classification based on dual-channel dilated convolution neural network[J]. Laser & Optoelectronics Progress, 57, 122803(2020).
[15] Li Y, Zhang H K, Shen Q. Spectral-spatial classification of hyperspectral imagery with 3D convolutional neural network[J]. Remote Sensing, 9, 67(2017).
[16] Zhang M H, Zou Y Q, Song W et al. GGCN: GPU-based hyperspectral image classification algorithm[J]. Laser & Optoelectronics Progress, 57, 201101(2020).
[17] Yan M J, Su X Y. Hyperspectral image classification based on three-dimensional dilated convolutional residual neural network[J]. Acta Optica Sinica, 40, 1628002(2020).
[18] Zhang X D, Wang T J, Yang Y. Classification of small-sized sample hyperspectral images based on multi-scale residual network[J]. Laser & Optoelectronics Progress, 57, 162801(2020).
[19] Zhang C J, Li G D, Du S H et al. Three-dimensional densely connected convolutional network for hyperspectral remote sensing image classification[J]. Journal of Applied Remote Sensing, 13, 016519(2019).
[20] Hu J, Shen L, Albanie S et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2011-2023(2020).
[21] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).
[22] 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).