[1] [1] LI X, DING M L, PIURICA A. Deep feature fusion via two-stream convolutional neural network for hyperspectral image classification［J］. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(4): 2615-2629.

[2] [2] GUO H, LIU J, XIAO Z, et al. Deep CNN-based hyperspectral image classification using discriminative multiple spatial-spectral feature fusion［J］. Remote Sensing Letters, 2020, 11(9): 827-836.

[3] [3] ZHAO W Zh, DU Sh H. 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.

[4] [4] CHEN Y Sh, LIN Zh H, ZHAO X, et al. Deep learning-based classification of hyperspectral data ［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote, 2014, 7(6): 2094-2107.

[5] [5] LIU P, CHOO K K R, WANG L, et al. SVM or deep learning? A comparative study on remote sensing image classification ［J］. Soft Computing, 2017, 21(23): 7053-7065.

[6] [6] ZHONG P, GONG Zh Q, LI Sh T, et al. Learning to diversify deep belief networks for hyperspectral image classification ［J］. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(6): 3516-3530.

[7] [7] ZHANG M M, LI W, DU Q. Diverse region-based CNN for hyperspectral image classification［J］. IEEE Transactions on Image Processing, 2018, 27(6): 2623-2634.

[8] [8] XU Y H, ZHANG L P, DU B, et al. Spectral-spatial unified networks for hyperspectral image classification ［J］. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(10): 5893-5909.

[9] [9] PAOLETTI M E, HAUT J M, FERNANDEZ-BELTRAN R, et al. Capsule networks for hyperspectral image classification［J］. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(4): 2145-2160.

[10] [10] ZHANG H K, LI Y, ZHANG Y Zh, et al. Spectral-spatial classification of hyperspectral imagery using a dual-channel convolutional neural network［J］. Remote Sensing Letters, 2017, 8(5): 438-447.

[11] [11] ZHONG Z L, LI J, LUO Zh M, et al. Spectral-spatial residual network for hyperspectral image classification: A 3-D deep learning framework［J］. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(2): 847-858.

[12] [12] LI J Y, LIANG B X, WANG Y H. A hybrid neural network for hyperspectral image classification［J］. Remote Sensing Letters, 2020, 11(1): 96-105.

[13] [13] LI W, WU G D, ZHANG F, et al. Hyperspectral image classification using deep pixel-pair features ［J］. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(2): 844-853.

[14] [14] CHEN Y Sh, JIANG H L, LI Ch Y, 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.

[15] [15] ROY S K, KRISHNA G, DUBEY S R, et al. HybridSN: Exploring 3-D-2-D CNN feature hierarchy for hyperspectral image classification［J］. IEEE Geoscience and Remote Sensing Letters, 2020, 17(2): 277-281.

[16] [16] GUAN Sh H, YANG G, LI H, et al. Hyperspectral image classification based on 3-D convolutional recurrent neural network［J］.Laser Technology, 2020, 44(4): 485-491(in Chinese).

[17] [17] LI Y, ZHANG H K, SHEN Q. Spectral-spatial classification of hyperspectral imagery with 3-D convolutional neural network ［J］. Remote Sensing, 2017, 9(1), 67.

[18] [18] MAKANTASIS K, KARANTZALOS K, DOULAMIS A, et al. Deep supervised learning for hyperspectral data classification through convolutional neural networks［C］//2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). New York, USA: IEEE, 2015: 4959-4962.

[19] [19] HANG R L, LI Zh, GHAMISI P, et al. Classification of hyperspectral and LiDAR data using coupled CNNs ［J］. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(7): 4939-4950.

[20] [20] HE M Y, LI B, CHEN H H. Multi-scale 3-D deep convolutional neural network for hyperspectral image classification ［C］// 2017 IEEE International Conference on Image Processing (ICIP). New York, USA: IEEE, 2017: 3904-3908.

[21] [21] MELGANI F, BRUZZONE L. Classification of hyperspectral remote sensing images with support vector machines ［J］. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(8): 1778-1790.