[2] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 521, 436-444(2015).
[4] Camps-Valls G, Bruzzone L. Kernel-based methods for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 43, 1351-1362(2005).
[5] Gao L R, Li J, Khodadadzadeh M et al. Subspace-based support vector machines for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 12, 349-353(2015).
[6] Xing C, Ma L, Yang X Q. Stacked denoise autoencoder based feature extraction and classification for hyperspectral images[J]. Journal of Sensors, 2016, 3632943(2016).
[7] Chen Y S, Zhao X, Jia X P. Spectral-spatial classification of hyperspectral data based on deep belief network[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 2381-2392(2015).
[8] Cao J, Chen Z, Wang B. Deep convolutional networks with superpixel segmentation for hyperspectral image classification. [C]//2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 10-15, 2016, Beijing, China. New York: IEEE, 3310-3313(2016).
[10] Chen Y S, Jiang H L, Li C Y et al. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 54, 6232-6251(2016).
[11] Dey N, Hong S, Ach T et al. Tensor decomposition of hyperspectral images to study autofluorescence in age-related macular degeneration[J]. Medical Image Analysis, 56, 96-109(2019).
[12] Ahmadi S A, Mehrshad N, Razavi S M. Noise reduction and feature extraction based on low-rank representation and pairwise constraint preserving for hyperspectral images[J]. International Journal of Remote Sensing, 40, 8236-8269(2019).
[13] Rahmani V, Rostami V. Adaptive color mapping for NAO robot using neural network[J]. Advances in Computer Science: an International Journal, 3, 66-71(2014).