[1] Balas C, Epitropou G, Tsapras A, et al. Hyperspectral imaging and spectral classification for pigment identification and mapping in paintings by El Greco and his workshop [J]. Multimedia Tools and Applications, 2018, 77: 9737-9751. doi:  10.1007/s11042-017-5564-2
[2] Amer R, Mezayen A A, Hasanein M. ASTER spectral analysis for alteration minerals associated with gold mineralization [J]. Ore Geology Reviews, 2016, 75: 239-251. doi:  10.1016/j.oregeorev.2015.12.008
[3] Hecker C, Ruitenbeek F, Werff H, et al. Spectral absorption feature analysis for finding ore: A tutorial on using the method in geological remote sensing [J]. IEEE Geoscience and Remote Sensing Magazine, 2019, 7(2): 51-71. doi:  10.1109/MGRS.2019.2899193
[4] Okada N, Maekawa Y, Owada N, et al. Automated identification of mineral types and grain size using hyperspectral imaging and deep learning for mineral processing [J]. Minerals, 2020, 10(9): 809. doi:  10.3390/min10090809
[5] Deng Kewang, Zhao Huijie, Li Na, et al. Identification of minerals in hyperspectral imagery based on the attenuation spectral absorption index vector using a multilayer perceptron [J]. Remote Sensing Letters, 2021, 12(5): 449-458. doi:  10.1080/2150704X.2021.1903612
[6] Zhang Minghua, Zou Yaqing, Song Wei, et al. GGCN: GPU-based hyperspectral image classification algorithm [J]. Laser & Optoelectronics Progress, 2020, 57(20): 231-237. (in Chinese)
[7] Denil M, Shakibi B, Dinh L, et al. Predicting parameters in deep learning [J]. Neural Information Processing Systems (NIPS), 2012: 1097-1105.
[8] Han S, Mao H, Dally W. Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding [J]. Fiber, 2015, 56(4): 3-7.
[9] Cheng Yu, Wang Duo, Zhou Pan, et al. A survey of model compression and acceleration for deep neural networks [J]. IEEE Signal Processing Magazine, 2017, 35(1): 126-136.
[10] Wang Yulong, Zhang Xiao, Xie Lingxi, et al. Pruning from scratch[C]// Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(7): 12273-12280.
[11] Hassibi B, Stork D, Wolff G. Optimal brain surgeon and general network pruning[C]//IEEE International Conference on Neural Networks, 1993: 293-299.
[12] Cun Y, Denker J, Solla S. Optimal brain damage[J]. Advances in Neural Information Processing Systems, 1990, 2: 598-605.
[13] Hassibi B. Second order derivatives for network pruning: Optimal brain surgeon [J]. Advances in Neural Information Processing Systems, 1992, 5: 164-171.
[14] Roy S, Panda P, Srinivasan G, et al. Pruning filters while training for efficiently optimizing deep learning networks[C]//2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020: 1-7.
[15] Guo Yiwen, Yao Anbang, Chen Yurong. Dynamic network surgery for efficient DNNs[C]//Neural Information Processing Systems, 2016: 1387-1395.
[16] Luo Jianhao, Wu jianxin, Lin Weiyao. ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression[C]//IEEE International Conference on Computer Vision (ICCV), 2017: 5068-5076.
[17] Hu Hengyuan, Peng Rui, Tai Yuwing, et al. Network trimming: A data-driven neuron pruning approach towards efficient deep architectures [J]. arXiv preprint arXiv, 2016: 1607.03250.
[18] Clark R N, Swayze G A, Livo K E, et al. Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems [J]. Journal of Geophysical Research, 2003, 108(E12): 5131.