[1] 孙家柄. 遥感原理与应用[M]. 2版. 武汉: 武汉大学出版社, 2009.

    Sun JB. Principles and applications of remote sensing[M]. 2nd ed. Wuhan: Wuhan University Press, 2009.

[2] Cheriyadat A M. Unsupervised feature learning for aerial scene classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 52(1): 439-451.

[3] Luo B, Zhang L. Robust autodual morphological profiles for the classification of high-resolution satellite images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 52(2): 1451-1462.

[4] 李大威, 杨风暴, 王肖霞. 基于随机森林与D-S证据合成的多源遥感分类研究[J]. 激光与光电子学进展, 2016, 53(3): 031001.

    Li D W, Yang F B, Wang X X. Multisource remote sensing classification based on random forest and adaptive weighted D-S evidence synthesis[J]. Laser & Optoelectronics Progress, 2016, 53(3): 031001.

[5] 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.

[6] KrizhevskyA, SutskeverI, Hinton GE. ImageNet classification with deep convolutional neural networks[C]// Proceedings of the 25 th International Conference on Neural Information Processing Systems , 2012: 1097- 1105.

[7] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(4): 640-651.

[8] 陈洋, 范荣双, 王竞雪, 等. 结合最小噪声分离变换和卷积神经网络的高分辨影像分类方法[J]. 激光与光电子学进展, 2017, 54(10): 102801.

    Chen Y, Fan R S, Wang J X, et al. High resolution image classification method combining with minimum noise fraction rotation and convolution neural network[J]. Laser & Optoelectronics Progress, 2017, 54(10): 102801.

[9] 邓曾, 李丹, 柯樱海, 等. 基于改进SVM算法的高分辨率遥感影像分类[J]. 国土资源遥感, 2016, 28(3): 12-18.

    Deng Z, Li D, Ke Y H, et al. An improved SVM algorithm for high spatial resolution remote sensing image classification[J]. Remote Sensing for Land and Resources, 2016, 28(3): 12-18.

[10] Yu D, Deng L. Deep learning and its applications to signal and information processing[J]. IEEE Signal Processing Magazine, 2010, 28(1): 145-154.

[11] MikolovT, ZweigG. Context dependent recurrent neural network language model: MSR-TR-2012-92[R]. Miami: 2012 IEEE Spoken Language Technology Workshop, 2012: 234- 239.

[12] 何小飞, 邹峥嵘, 陶超, 等. 联合显著性和多层卷积神经网络的高分影像场景分类[J]. 测绘学报, 2016, 45(9): 1073-1080.

    He X F, Zou Z R, Tao C, et al. Combined saliency with multi-convolutional neural network for high resolution remote sensing scene classification[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(9): 1073-1080.

[13] IoffeS, SzegedyC. Batch normalization: accelerating deep network training by reducing internal covariate shift[C]// 32 nd International Conference on Machine Learning , 2015: 448- 456.

[14] SimonyanK, ZissermanA. Very deep convolutional networks for large-scale image recognition[J]. Computer Science:2014arXiv1409. 1556S.

[15] 王更, 王光辉, 杨化超. 融合颜色-纹理模型的均值漂移分割算法[J]. 测绘科学, 2015, 40(8): 108-112.

    Wang G, Wang G H, Yang H C. Improved Mean-Shift segmentation algorithm combining with color-texture pattern[J]. Science of Surveying and Mapping, 2015, 40(8): 108-112.

[16] 周家香, 朱建军, 梅小明, 等. 多维特征自适应MeanShift遥感图像分割方法[J]. 武汉大学学报(信息科学版), 2012, 37(4): 419-422.

    Zhou J X, Zhu J J, Mei X M, et al. An adaptive MeanShift segmentaion method of remote sensing image based on multi-dimension features[J]. Geomatics and Information Science of Wuhan University, 2012, 37(4): 419-422.

[17] Cheng Y Z. Mean shift, mode seeking, and clustering[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 1995, 17(8): 790-799.