[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] Ji R R, Gao Y, Hong R C et al. Spectral-spatial constraint hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 52, 1811-1824(2014).

[3] Pal M, Foody G M. Feature selection for classification of hyperspectral data by SVM[J]. IEEE Transactions on Geoscience and Remote Sensing, 48, 2297-2307(2010).

[4] Fang L Y, Li S T, Kang X D et al. Spectral-spatial hyperspectral image classification via multiscale adaptive sparse representation[J]. IEEE Transactions on Geoscience and Remote Sensing, 52, 7738-7749(2014).

[5] Zhang H Y, Li J Y, Huang Y C et al. A nonlocal weighted joint sparse representation classification method for hyperspectral imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 2056-2065(2014).

[6] Fang L Y, Li S T, Kang X D et al. Spectral-spatial classification of hyperspectral images with a superpixel-based discriminative sparse model[J]. IEEE Transactions on Geoscience and Remote Sensing, 53, 4186-4201(2015).

[7] Zhou Y C, Peng J T. Chen C L P. Extreme learning machine with composite kernels for hyperspectral image classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 2351-2360(2015).

[8] Zhang H, Chen C H. Aurora sequence classification based on deep learning[J]. Laser & Optoelectronics Progress, 55, 111504(2018).

[9] Chen Y S, Lin Z H, Zhao X et al. Deep learning-based classification of hyperspectral data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 2094-2107(2014).

[10] Zhong P, Gong Z Q, Li S T et al. Learning to diversify deep belief networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 55, 3516-3530(2017).

[11] 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).

[12] 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).

[13] Bioucas-Dias J M, Plaza A, Dobigeon N et al. . Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5, 354-379(2012).

[14] Tao C, Pan H B, Li Y S et al. Unsupervised spectral-spatial feature learning with stacked sparse autoencoder for hyperspectral imagery classification[J]. IEEE Geoscience and Remote Sensing Letters, 12, 2438-2442(2015).

[15] Dong A G, Li J X, Zhang B et al. Hyperspectral image classification algorithm based on spectral clustering and sparse representation[J]. Acta Optica Sinica, 37, 0828005(2017).

[16] Tao C, Tang Y Q, Fan C et al. Hyperspectral imagery classification based on rotation-invariant spectral-spatial feature[J]. IEEE Geoscience and Remote Sensing Letters, 11, 980-984(2014).

[17] Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks. [C]∥Proceedings of the 13th International Conference on Artificial Intelligence and Statistic, June 6-10, 2011, Pittsburgh, Pennsylvania. Cambridge: PMLR, 249-256(2011).