[1] 陈君颖, 田庆久. 高分辨率遥感植被分类研究[J]. 遥感学报, 2007, 11(2): 221-227.

    Chen J Y, Tian Q J. Vegetation classification based on high-resolution satellite image[J]. Journal of Remote Sensing, 2007, 11(2): 221-227.

[2] 浮媛媛, 赵云升, 赵文利, 等. 基于多源亮度温度的城市典型植被分类研究[J]. 激光与光电子学进展, 2015( 7): 072801.

    Fu YY, Zhao YS, Zhao WL, et al. Studies of typical urban vegetation classification based on brightness temperature from multiple sources[J]. Laser & Optoelectronics Progress, 2015( 7): 072801.

[3] Oberbauer S F, Tenhunen J D, Reynolds J F. Environmental effects on CO2 efflux from water track and tussock tundra in arctic Alaska, USA[J]. Arctic and Alpine Research, 1991, 23(2): 162-169.

[4] Jung M, Henkel K, Herold M, et al. Exploiting synergies of global land cover products for carbon cycle modeling[J]. Remote Sensing of Environment, 2006, 101(4): 534-553.

[5] Lecun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.

[6] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks: advances in neural information processing systems[J]. Communications of the ACM, 2017, 60(6): 84-90.

[7] Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507.

[8] BengioY, DelalleauO. On the expressive power of deep architectures[C]// International Conference on Algorithmic Learning Theory, Springer-Verlag, 2011: 18- 36.

[9] 黄鸿, 何凯, 郑新磊, 等. 基于深度学习的高光谱图像空-谱联合特征提取[J]. 激光与光电子学进展, 2017, 54(10): 101001.

    Huang H, He K, Zheng X L, et al. Spatial-spectral feature extraction of hyperspectral imagery based on deep learning[J]. Laser & Optoelectronics Progress, 2017, 54(10): 101001.

[10] JaitlyN, NguyenP, SeniorA, et al. Application of pretrained deep neural networks to large vocabulary conversational speech recognition[C]// 13th Annual Conference of the International Speech Communication Association, 2012( 3): 2577- 2580.

[11] TokarczykP, MontoyaJ, SchindlerK. An evaluation of feature learning methods for high resolution image classification[J]. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012( 1-3): 389- 394.

[12] 刘大伟, 韩玲, 韩晓勇. 基于深度学习的高分辨率遥感影像分类研究[J]. 光学学报, 2016, 36(4): 0428001.

    Liu D W, Han L, Han X Y. High spatial resolution remote sensing image classification based on deep learning[J]. Acta Optica Sinica, 2016, 36(4): 0428001.

[13] 吕启, 窦勇, 牛新, 等. 基于DBN模型的遥感图像分类[J]. 计算机研究与发展, 2014, 51(9): 1911-1918.

    Lü Q, Dou Y, Niu X, et al. Remote sensing image classificationbased on DBN model[J]. Journal of Computer Research and Development, 2014, 51(9): 1911-1918.

[14] 李新国, 黄晓晴. 一种基于DBN的高光谱遥感图像分类方法[J]. 电子测量技术, 2016, 39(7): 81-86.

    Li X G, Huang X Q. Deep neural networks based on hyperspectral image classification[J]. Electronic Measurement Technology, 2016, 39(7): 81-86.

[15] Hinton G E. Training products of experts by minimizing contrastive divergence[J]. Neural Computation, 2002, 14(8): 1771-1800.

[16] 李娜, 包妮沙, 吴立新, 等. 面向对象矿区复垦植被分类最优分割尺度研究[J]. 测绘科学, 2016, 41(4): 66-71, 76.

    Li N, Bao N S, Wu L X, et al. Study of optimal segmentation scale in object based classification for rehabilitated vegetation in coal mining site[J]. Science of Surveying and Mapping, 2016, 41(4): 66-71, 76.

[17] Zhuang X, Engel B A, Xiong X P, et al. Analysis of classification results of remotely sensed data and evaluation of classification algorithms[J]. Photogrammetric Engineering and Remote Sensing, 1995, 61(4): 427-433.

[18] 祝振江. 基于面向对象分类法的高分辨率遥感影像矿山信息提取应用研究[D]. 北京: 中国地质大学( 北京), 2010.

    Zhu ZJ. Study on mine area information extraction based on object-oriented high-resolution remote sensing image classification and its application[D]. Beijing: China University of Geosciences, 2010.