[1] Zhang B. Advancement of hyperspectral image processing and information extraction[J]. Journal of Remote Sensing, 20, 1062-1090(2016).

[2] Bioucas-Dias J M, Plaza A, Camps-Valls G et al. Hyperspectral remote sensing data analysis and future challenges[J]. IEEE Geoscience and Remote Sensing Magazine, 1, 6-36(2013).

[3] Camps-Valls G, Bruzzone L. Kernel-based methods for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 43, 1351-1362(2005).

[4] Belgiu M, Draguţ L. Random forest in remote sensing: a review of applications and future directions[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24-31(2016).

[5] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 521, 436-444(2015).

[6] Yan M, Zhao H D, Li Y H et al. Multi-classification and recognition of hyperspectral remote sensing objects based on convolutional neural network[J]. Laser & Optoelectronics Progress, 56, 021702(2019).

[7] Liu Y Z, Jiang Z Q, Ma F et al. Hyperspectral image classification based on hypergraph and convolutional neural network[J]. Laser & Optoelectronics Progress, 56, 111007(2019).

[8] Leng J B, Li T, Bai G et al. Cube-CNN-, 1027-1034(2016).

[9] Li P, Guan G X, Wu T X et al. GPU optimization of hyperspectral real-time anomaly detection based on Cholesky decomposition[J]. Transducer and Microsystem Technology, 38, 7-10(2019).

[10] Jiang X R, Wei W[J]. Application of CUDA technology in digital image matching Modern Information Technology, 2019, 61-63.

[11] Shen T, Hu F[J]. Acceleration of CNN on GPU Applications of IC, 2017, 18-22.

[12] [12] DongH, LiT, Leng JB, et al.GCN: GPU-based cube CNN framework for hyperspectral image classification[C]//2017 46th International Conference on Parallel Processing (ICPP). 14-17 Aug. 2017, Bristol, UK. New York: IEEE Press, 2017: 41- 49.

[13] Nakasato N. A fast GEMM implementation on the cypress GPU[J]. ACM SIGMETRICS Performance Evaluation Review, 38, 50-55(2011).