[1] Liu D W, Han L, Han X Y. High spatial resolution remote sensing image classification based on deep learning[J]. Acta Optica Sinica, 36, 0428001(2016).

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

[3] Lin Z, Chen Y, Zhao X et al. -11-09)[2019-07-14], top/abs/1511, 02916(2015). https://arxiv.xilesou.

[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).

[9] Song B Q, Li J, Dalla Mura M et al. Remotely sensed image classification using sparse representations of morphological attribute profiles[J]. IEEE Transactions on Geoscience and Remote Sensing, 52, 5122-5136(2014).

[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).

[14] Zhong Y F, Zhang L P. An adaptive artificial immune network for supervised classification of multi-/hyperspectral remote sensing imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 50, 894-909(2012).

[15] Ye Z, Bai L. Hyperspectral image classification based on principal component analysis and local binary patterns[J]. Laser & Optoelectronics Progress, 54, 111006(2017).

[16] Yu C Y, Zhao M, Song M P et al. Hyperspectral image classification method based on targets constraint and spectral-spatial iteration[J]. Acta Optica Sinica, 38, 0628003(2018).

[17] Huang H, He K, Zheng X L et al. Spatial-spectral feature extraction of hyperspectral image based on deep learning[J]. Laser & Optoelectronics Progress, 54, 101001(2017).