[1] Li S H, Wang J L, Bi Y et al. A review of methods for classification of remote sensing images[J]. Remote Sensing for Land & Resources, 17, 1-6(2005).

[2] Shan B H, Huo X Y, Liu Y. A stereovision measurement method using epipolar constraint to correct digital image correlation matching[J]. Chinese Journal of Lasers, 44, 0804003(2017).

[3] Zhao F Z, Liang H Y, Wu X L et al. Active contour segmentation model based on local and global Gaussian fitting[J]. Laser & Optoelectronics Progress, 54, 051006(2017).

[4] Song Y, Wu Y Q, Bi S B. Satellite remote sensing cloud image segmentation using edge corrected CV model[J]. Acta Optica Sinica, 34, 0901004(2014).

[5] Cheriyadat A M. Unsupervised feature learning for aerial scene classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 52, 439-451(2014). http://ieeexplore.ieee.org/document/6472060

[6] 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).

[7] Alberga V. A study of land cover classification using polarimetric SAR parameters[J]. International Journal of Remote Sensing, 28, 3851-3870(2007). http://www.tandfonline.com/doi/full/10.1080/01431160601075541

[8] Hagner O, Reese H. A method for calibrated maximum likelihood classification of forest types[J]. Remote Sensing of Environment, 110, 438-444(2007). http://www.sciencedirect.com/science/article/pii/S0034425707001757

[9] Niu X, Ban Y F. Multi-temporal RADARSAT-2 polarimetric SAR data for urban land-cover classification using an object-based support vector machine and a rule-based approach[J]. International Journal of Remote Sensing, 34, 1-26(2013). http://www.tandfonline.com/doi/full/10.1080/01431161.2012.700133

[10] Heermann P D, Khazenie N. Classification of multispectral remote sensing data using a back-propagation neural network[J]. IEEE Transactions on Geoscience and Remote Sensing, 30, 81-88(1992). http://ieeexplore.ieee.org/iel1/36/3503/00124218.pdf

[11] Pal M, Mather P M. An assessment of the effectiveness of decision tree methods for land cover classification[J]. Remote Sensing of Environment, 86, 554-565(2003). http://www.sciencedirect.com/science/article/pii/S0034425703001329

[12] Bengio Y. Learning deep architectures for AI[M]. Foundations and Trends® in Machine Learning, 2, 1-127(2009).

[13] Du P J, Xia J S, Xue Z H et al. Review of hyperspectral remote sensing image classification[J]. Journal of Remote Sensing, 20, 236-256(2016).

[14] Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 640-651(2017). http://www.tandfonline.com/servlet/linkout?suffix=CIT0044&dbid=16&doi=10.1080%2F15481603.2018.1426091&key=10.1109%2FCVPR.2015.7298965

[15] Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation[M]. ∥Navab N, Hornegger J, Wells W, et al. Medical image computing and computer-assisted intervention. Lecture notes in computer science. Cham: Springer, 9351, 234-241(2015).

[16] Chen L C, George P, Iasonas K, fully connected CRFs[J/OL] et al. -06-07)[2018-12-01]. https:∥arxiv., org/abs/1412, 7062(2016).

[17] Chen L C, Papandreou G, Kokkinos I et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834-848(2018). http://doi.ieeecomputersociety.org/10.1109/TPAMI.2017.2699184

[18] Chen L C, Papandreou G, Schroff F et al. -12-05)[2018-12-01]. https:∥arxiv., org/abs/1706, 05587(2017).

[19] Badrinarayanan V, Kendall A, Cipolla R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 2481-2495(2017). http://www.ncbi.nlm.nih.gov/pubmed/28060704

[20] Chen L C, Zhu Y K, Papandreou G et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[M]. ∥Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science. Cham: Springer, 11211, 833-851(2018).

[21] Grauman K, Darrell T. The Pyramid match kernel: discriminative classification with sets of image features. [C]∥Tenth IEEE International Conference on Computer Vision (ICCV'05), October 17-21, 2005, Beijing, China. New York: IEEE, 8824338(2005).

[22] Lazebnik S, Schmid C, Ponce J. Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. CVPR'06), June 17-22, 2006, New York, NY, USA. New York: IEEE(2006).

[23] He K M, Zhang X Y, Ren S Q et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[M]. ∥Fleet D, Pajdla T, Schiele B, et al. Computer vision-ECCV 2014. Lecture notes in computer science. Cham: Springer, 8691, 346-361(2014).

[24] Ge W Y, Liu G Y. Unsupervised classification of high-resolution remote-sensing images under edge constraints[J]. Proceedings of SPIE, 10609, 106091C(2017).