[1] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe. Nevada:ACM, 2012:1097-1105. [2] ZANG Liangpei, ZHANG Lefei, DU Bo. Deep learning for remote sensing data:a technical tutorial on the state of the art[J]. IEEE Geoscience and Remote Sensing Magazine, 2016, 4(2):22-40. [3] 闫利, 江维薇. 一种利用结构特征的高分辨率遥感影像种植园自动提取方法[J]. 测绘学报, 2016, 45(9):1065-1072. DOI:10.11947/j.AGCS.2016.20150511. YAN Li, JIANG Weiwei. A structure feature for automatic extraction of plantation from high-resolution remote sensing imagery[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(9):1065-1072. DOI:10.11947/j.AGCS.2016.20150511. [4] 郭军士. 基于改进形态学指数的ZY3影像建筑物和阴影的提取及应用[D]. 西安:西安科技大学, 2015. GUO Junshi. Extraction and application of building and shadow of ZY3 image based on improving morphological index[D]. Xi'an:Xi'an University of Science and Technology, 2015. [5] 张莹莹. 高分辨率遥感影像中典型道路提取方法研究[D]. 哈尔滨:哈尔滨工业大学, 2016. ZHANG Yingying. Research on methods of typical road extraction from high-resolution remote sensing image[D]. Harbin:Harbin Institute of Technology, 2016. [6] 李航. 统计学习方法[M]. 北京:清华大学出版社, 2012. LI Hang. Statistical learning method[M]. Beijing:Tsinghua University Press, 2012. [7] 许夙晖, 慕晓冬, 赵鹏, 等. 利用多尺度特征与深度网络对遥感影像进行场景分类[J]. 测绘学报, 2016, 45(7):834-840. DOI:10.11947/j.AGCS.2016.20150623. XU Suhui, MU Xiaodong, ZHAO Peng, et al. Scene classification of remote sensing image based on multi-scale feature and deep neural network[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(7):834-840. DOI:10.11947/j.AGCS.2016.20150623. [8] KAMPFFMEYER M, SALBERG A B, JENSSEN R. Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Las Vegas, NV:IEEE, 2016:680-688. [9] ZHANG Fan, DU Bo, ZHANG Liangpei, et al. Weakly supervised learning based on coupled convolutional neural networks for aircraft detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(9):5553-5563. [10] LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11):2278-2324. [11] HU Fan, XIA Guisong, HU Jingwen, et al. Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery[J]. Remote Sensing, 2015, 7(11):14680-14707. [12] ZHANG Fan, DU Bo, ZHANG Liangpei. Saliency-guided unsupervised feature learning for scene classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(4):2175-2184. [13] SHELHAMER E, LONG J, DARRELL T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4):640-651. [14] RONNEBERGER O, FISCHER P, BROX T. U-net:convolutional networks for biomedical image segmentation[C]//Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich, Germany:Springer, 2015:234-241. [15] 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, 2017, 39(12):2481-2495. [16] IOFFE S, SZEGEDY C. Batch normalization:accelerating deep network training by reducing internal covariate shift[C]//Proceedings of the 32nd International Conference on Machine Learning. Lille, France:[s.n.], 2015:448-456. [17] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Columbus, OH:IEEE, 2014:215-228. [18] GIRSHICK R. Fast R-CNN[C]//Proceedings of 2015 IEEE International Conference on Computer Vision. Santiago, Chile:IEEE, 2015. [19] REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN:towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6):1137-1149. [20] HE Kaiming, GKIOXARI G, DOLLÁR P, et al. Mask R-CNN[C]//Proceedings of the 2017 IEEE International Conference on Computer Vision. Venice, Italy:IEEE, 2017:2980-2988. [21] CHEN Fen, REN Ruilong, VAN DE VOORDE T, et al. Fast automatic airport detection in remote sensing images using convolutional neural networks[J]. Remote Sensing, 2018, 10(3):443. [22] HOLLAND J H. Adaptation in natural and artificial systems[M]. Cambridge, Massachusetts:MIT Press, 1992. [23] 李明. 遗传算法的改进及其在优化问题中的应用研究[D]. 长春:吉林大学, 2004. LI Ming. The study on improved genetic algorithm and its application in optimization questions[D]. Changchun:Jilin University, 2004. [24] CLEVERT D A C, UNTERTHINER T, HOCHREITER S. Fast and accurate deep network learning by exponential linear units (ELUs)[R]. International Conference on Learning Representations 2016. San Juan:ICLR, 2016. [25] KINGMA D P, BA J. ADAM:a method for stochastic optimization[C]//Proceedings of International Conference on Learning Representations 2015. San Diego:ICLR, 2015:321-335. [26] TAN P, STEINBACH M, KUMAR V. 数据挖掘导论[M]. 范明, 范宏建, 译. 北京:人民邮电出版社,2011. TAN P, STEINBACH M, KUMAR V. Introduction to data mining[M]. FAN Ming, FAN Hongjian, trans. Beijing:Post & Telecom Press, 2011. [27] CHANG C C, LIN C J. LIBSVM:a library for support vector machines[J]. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3):No.27. [28] MENDOZA H, KLEIN A, FEURER M, et al. Towards automatically-tuned neural networks[C]//Proceedings of JMLR:Workshop and Conference Proceedings.[S.l.]:JMLR, 2016:58-65. [29] KANDASAMY K, NEISWANGER W, SCHNEIDER J, et al. Neural architecture search with Bayesian optimisation and optimal transport[C]//Proceedings of the 32nd Conference on Neural Information Processing Systems. Montréal, Canada:[s.n.], 2018. [30] ASSUNÇO F, LOURENÇO N, MACHADO P, et al. DENSER:deep evolutionary network structured representation[J]. Genetic Programming and Evolvable Machines, 2019, 20(1):5-35. [31] DUFOURQ E, BASSET B A. EDEN:evolutionary deep networks for efficient machine learning[C]//Proceedings of 2017 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference. Bloemfontein, South Africa:[s.n.], 2017. |