[1] 张兵. 高光谱图像处理与信息提取前沿[J]. 遥感学报, 2016, 20(5): 1062-1090.

    Zhang B. Advancement of hyperspectral image processing and information extraction[J]. Journal of Remote Sensing, 2016, 20(5): 1062-1090.

[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, 2013, 1(2): 6-36.

[3] Camps-Valls G, Bruzzone L. Kernel-based methods for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(6): 1351-1362.

[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, 2016, 114: 24-31.

[5] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.

[6] 闫苗, 赵红东, 李宇海, 等. 基于卷积神经网络的高光谱遥感地物多分类识别[J]. 激光与光电子学进展, 2019, 56(2): 021702.

    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, 2019, 56(2): 021702.

[7] 刘玉珍, 蒋政权, 马飞, 等. 基于超图和卷积神经网络的高光谱图像分类[J]. 激光与光电子学进展, 2019, 56(11): 111007.

    Liu Y Z, Jiang Z Q, Ma F, et al. Hyperspectral image classification based on hypergraph and convolutional neural network[J]. Laser & Optoelectronics Progress, 2019, 56(11): 111007.

[8] Leng JB, LiT, BaiG, et al.Cube-CNN-SVM: a novel hyperspectral image classification method[C]//2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI). 6-8 Nov. 2016, San Jose, CA, USA. New York: IEEE Press, 2016: 1027- 1034.

[9] 李萍, 关桂霞, 吴太夏, 等. 基于Cholesky分解的高光谱实时异常探测的GPU优化[J]. 传感器与微系统, 2019, 38(3): 7-10.

    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, 2019, 38(3): 7-10.

[10] 姜雪茸, 魏威. CUDA技术在数字图像匹配中的应用[J]. 现代信息科技, 2019( 18): 61- 63.

    Jiang XR, WeiW. Application of CUDA technology in digital image matching[J]. Modern Information Technology, 2019( 18): 61- 63.

[11] 沈恬, 胡飞. 卷积神经网络在图形处理GPU芯片上的优化[J]. 集成电路应用, 2017( 6): 18- 22.

    ShenT, HuF. Acceleration of CNN on GPU[J]. Applications of IC, 2017( 6): 18- 22.

[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, 2011, 38(4): 50-55.