[1] 崔颖, 徐凯, 陆忠军, 等. 主动学习策略融合算法在高光谱图像分类中的应用[J]. 通信学报, 2018, 39(4): 2018067.

    Cui Y, Xu K, Lu Z J, et al. Combination strategy of active learning for hyperspectral images classification[J]. Journal on Communications, 2018, 39(4): 2018067.

[2] 董安国, 李佳逊, 张蓓, 等. 基于谱聚类和稀疏表示的高光谱图像分类算法[J]. 光学学报, 2017, 37(8): 0828005.

    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, 2017, 37(8): 0828005.

[3] 侯榜焕, 姚敏立, 王榕, 等. 面向高光谱图像分类的空谱半监督局部判别分析[J]. 光学学报, 2017, 37(7): 0728002.

    Hou B H, Yao M L, Wang R, et al. Spatial-spectral semi-supervised local discriminant analysis for hyperspectral image classification[J]. Acta Optica Sinica, 2017, 37(7): 0728002.

[4] 于纯妍, 赵猛, 宋梅萍, 等. 基于目标约束与谱空迭代的高光谱图像分类方法[J]. 光学学报, 2018, 38(6): 0628003.

    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, 2018, 38(6): 0628003.

[5] 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, 2016, 54(10): 6232-6251.

[6] Li Y, Zhang H K, Shen Q. Spectral-spatial classification of hyperspectral imagery with 3D convolutional neural network[J]. Remote Sensing, 2017, 9(1): 67.

[7] Wu H, Prasad S. Convolutional recurrent neural networks for hyperspectral data classification[J]. Remote Sensing, 2017, 9(3): 298.

[8] Zhao W Z, Du S H. Spectral-spatial feature extraction for hyperspectral image classification: a dimension reduction and deep learning approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(8): 4544-4554.

[9] Aptoula E, Ozdemir M C, Yanikoglu B. Deep learning with attribute profiles for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(12): 1970-1974.

[10] 张号逵, 李映, 姜晔楠. 深度学习在高光谱图像分类领域的研究现状与展望[J]. 自动化学报, 2018, 44(6): 961-977.

    Zhang H K, Li Y, Jiang Y N. Deep learning for hyperspectral imagery classification: the state of the art and prospects[J]. Acta Automatica Sinica, 2018, 44(6): 961-977.

[11] Goodfellow IJ, Pouget-AbadieJ, MirzaM, et al.Generative adversarial nets[C]∥Proceedings of the 27th International Conference on Neural Information Processing Systems, December 8-13, 2014, Montreal, Canada. Cambridge, MA,USA: MIT Press, 2014, 2: 2672- 2680.

[12] Zhan Y, Hu D, Wang Y T, et al. Semisupervised hyperspectral image classification based on generative adversarial networks[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(2): 212-216.

[13] Zhu L, Chen Y S, Ghamisi P, et al. Generative adversarial networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(9): 5046-5063.

[14] RadfordA, MetzL, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks[J/OL]. ( 2016-01-07)[2019-02-10]. https: ∥arxiv.org/abs/1511. 06434.

[15] 杨韶晟. 基于VAE的条件生成式对抗网络模型研究[D]. 长春: 吉林大学, 2018: 26- 29.

    Yang SS. Research on conditional generative adversarial networks model based on VAE[D]. Changchun: Jilin University, 2018: 26- 29.

[16] OdenaA, OlahC, ShlensJ. Conditional image synthesis with auxiliary classifier GANs[C]∥Proceedings of the 34th International Conference on Machine Learning, August 6-11, 2017, Sydney, Australis. Massachusetts: JMLR. org, 2017: 2642- 2651.

[17] 马晓瑞. 基于深度学习的高光谱影像分类方法研究[D]. 大连: 大连理工大学, 2017: 30- 31.

    Ma XR. Hyperspectral imagery classification based on deep learning[D]. Dalian: Dalian University of Technology, 2017: 30- 31.