Improved data-driven compressing method for hyperspectral mineral identification models

Kewang Deng; Huijie Zhao; Na Li; Hui Cai

doi:10.3788/IRLA20210252

[1] C Balas, G Epitropou, A Tsapras, et al. Hyperspectral imaging and spectral classification for pigment identification and mapping in paintings by El Greco and his workshop. Multimedia Tools and Applications, 77, 9737-9751(2018).

[2] R Amer, A A Mezayen, M Hasanein. ASTER spectral analysis for alteration minerals associated with gold mineralization. Ore Geology Reviews, 75, 239-251(2016).

[3] C Hecker, F Ruitenbeek, H Werff, et al. Spectral absorption feature analysis for finding ore: A tutorial on using the method in geological remote sensing. IEEE Geoscience and Remote Sensing Magazine, 7, 51-71(2019).

[4] N Okada, Y Maekawa, N Owada, et al. Automated identification of mineral types and grain size using hyperspectral imaging and deep learning for mineral processing. Minerals, 10, 809(2020).

[5] Kewang Deng, Huijie Zhao, Na Li, et al. Identification of minerals in hyperspectral imagery based on the attenuation spectral absorption index vector using a multilayer perceptron. Remote Sensing Letters, 12, 449-458(2021).

[6] Minghua Zhang, Yaqing Zou, Wei Song, et al. GGCN: GPU-based hyperspectral image classification algorithm. Laser & Optoelectronics Progress, 57, 231-237(2020).

[7] M Denil, B Shakibi, L Dinh, et al. Predicting parameters in deep learning. Neural Information Processing Systems (NIPS), 1097-1105(2012).

[8] S Han, H Mao, W Dally. Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. Fiber, 56, 3-7(2015).

[9] Yu Cheng, Duo Wang, Pan Zhou, et al. A survey of model compression and acceleration for deep neural networks. IEEE Signal Processing Magazine, 126-136(2017351).

[10] Wang Yulong, Zhang Xiao, Xie Lingxi, et al. Pruning from scratch[C] Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(7): 1227312280.

[11] Hassibi B, Stk D, Wolff G. Optimal brain surgeon general wk pruning[C]IEEE International Conference on Neural wks, 1993: 293299.

[12] Cun Y, Denker J, Solla S. Optimal brain damage[J]. Advances in Neural Infmation Processing Systems, 1990, 2: 598605.

[13] B Hassibi. Second order derivatives for network pruning: Optimal brain surgeon. Advances in Neural Information Processing Systems, 5, 164-171(1992).

[14] Roy S, Pa P, Srinivasan G, et al. Pruning filters while training f efficiently optimizing deep learning wks[C]2020 International Joint Conference on Neural wks (IJCNN). IEEE, 2020: 17.

[15] Guo Yiwen, Yao Anbang, Chen Yurong. Dynamic wk surgery f efficient DNNs[C]Neural Infmation Processing Systems, 2016: 13871395.

[16] Luo Jianhao, Wu jianxin, Lin Weiyao. Thi: A Filter Level Pruning Method f Deep Neural wk Compression[C]IEEE International Conference on Computer Vision (ICCV), 2017: 50685076.

[17] Hengyuan Hu, Rui Peng, Yuwing Tai, et al. Network trimming: A data-driven neuron pruning approach towards efficient deep architectures. arXiv preprint arXiv, 1607.03250(2016).

[18] R N Clark, G A Swayze, K E Livo, et al. Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems. Journal of Geophysical Research, 108, 5131(2003).