[1] 胡娟, 杨厚群, 杜欣然, 等. 小样本学习在高分遥感影像分类与识别中的应用[J]. 重庆邮电大学学报(自然科学版), 2022, 34(3): 410-422. Hu J, Yang H Q, Du X R, et al. Application of few-shot learning in high resolution remote sensing image classification and recognition [J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2022, 34(3): 410-422. (in Chinese)
[2] Yao X D, Guo Q, Li A. Light-weight cloud detection network for optical remote sensing images with attention-based Deeplab V3+ architecture [J]. Remote Sensing, 2021, 13(18): 3617.
[3] 叶利华, 王磊, 张文文, 等. 高分辨率光学遥感场景分类的深度度量学习方法[J]. 测绘学报, 2019, 48(6): 698-707. Ye L H, Wang L, Zhang W W, et al. Deep metric learning method for high resolution sensing image scene classification [J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(6): 698-707. (in Chinese)
[4] Rumelhart D E, Hinton G E, Williams R J. Learning representations by back-propagating errors [J]. Nature, 1986, 323(6088): 533-536.
[5] 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.
[6] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks [J]. Communications of the ACM, 2012, 60: 84-90.
[7] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition [DB/OL]. 2014[2023-03-21]. https://arxiv.org/abs/1409.1556.
[8] Szegedy C, Liu W, Jia Y Q, et al. Going deeper with convolutions [C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015: 1-9.
[9] Szegedy C, Ioffe S, Vanhoucke V, et al. Inception-v4, inception-resNet and the impact of residual connections on learning [DB/OL]. 2016[2023-03-21]. https://arxiv.org/abs/1602.07261.
[10] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation [C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015: 3431-3440.
[11] Liu W, Rabinovich A, Berg A C. ParseNet: looking wider to see better [DB/OL]. 2015[2023-03-21]. https://arxiv.org/abs/1506.04579.
[12] He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition [C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: 770-778.
[13] Zhang H, Wu C, Zhang Z, et al. ResNeSt: split-attention networks [DB/OL]. 2020[2023-03- 21]. https://arxiv.org/abs/2004.08955.
[14] Huang G, Liu Z, van der Maaten L, et al. Densely connected convolutional networks [DB/OL]. 2016[2023-03-21]. https://arxiv.org/abs/1608.06993.
[15] Sabour S, Frosst N, Hinton G E. Dynamic routing between capsules [DB/OL]. 2017[2023- 03-21]. https://arxiv.org/abs/1710.09829.
[16] Gao S H, Cheng M M, Zhao K, et al. Res2Net: a new multi-scale backbone architecture [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(2): 652-662.
[17] 刘媛. 高分辨率遥感影像建筑物屋顶识别方法研究[D]. 北京: 北京建筑大学, 2021.
[18] 林祥国, 张继贤, 李海涛, 等. 基于T型模板匹配半自动提取高分辨率遥感影像带状道路[J]. 武汉大学学报(信息科学版), 2009, 34(3): 293-296. Lin X G, Zhang J X, Li H T, et al. Semi-automatic extraction of ribbon road from high resolution remotely sensed imagery by a T-shaped template matching [J]. Geomatics and Information Science of Wuhan University, 2009, 34(3): 293-296.(in Chinese)
[19] Wentz E A, Nelson D, Rahman A, et al. Expert system classification of urban land use/cover for Delhi, India [J]. International Journal of Remote Sensing, 2008, 29(15): 4405-4427.
[20] 王芬, 郭擎, 葛小青. 深度递归残差网络的遥感图像空谱融合[J]. 遥感学报, 2021, 25(6): 1244-1256. Wang F, Guo Q, Ge X Q. Pan-sharpening by deep recursive residual network [J]. National Remote Sensing Bulletin, 2021, 25(6): 1244-1256.(in Chinese)
[21] 候玉婷, 王书功, 南卓铜. 基于知识规则的土地利用/土地覆被分类方法: 以黑河流域为例[J]. 地理学报, 2011, 66(4): 549-561. Hou Y T, Wang S G, Nan Z T. A rule-based land cover classification method for the Heihe River basin [J]. Acta Geographica Sinica, 2011, 66(4): 549-561.(in Chinese)
[22] Celik T. Unsupervised change detection in satellite images using principal component analysis and K-means clustering [J]. IEEE Geoscience and Remote Sensing Letters, 2009, 6(4): 772-776.
[23] Huang X, Zhang L P. An SVM ensemble approach combining spectral, structural, and semantic features for the classification of high-resolution remotely sensed imagery [J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(1): 257-272.
[24] Rodriguez-Galiano V F, Ghimire B, Rogan J, et al. An assessment of the effectiveness of a random forest classifier for land-cover classification [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2012, 67: 93-104.
[25] 骆剑承, 吴田军, 夏列钢. 遥感图谱认知理论与计算[J]. 地球信息科学学报, 2016, 18(5): 578-589. Luo J C, Wu T J, Xia L G. The theory and calculation of spatial-spectral cognition of remote sensing [J]. Journal of Geo-Information Science, 2016, 18(5): 578-589.(in Chinese)
[26] Guo Q, Li S J, Li A. An efficient dual spatial-spectral fusion network [J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-13.
[27] 周成虎, 骆剑承, 陈秋晓, 等. 遥感影像地学理解与分析[M]. 北京: 科学出版社, 1999.
[28] Blaschke T, Hay G J, Kelly M, et al. Geographic object-based image analysis-towards a new paradigm [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 87: 180-191.
[29] Blaschke T. Object based image analysis for remote sensing [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2010, 65(1): 2-16.
[30] 龚健雅, 季顺平. 摄影测量与深度学习[J]. 测绘学报, 2018, 47(6): 693-704. Gong J Y, Ji S P. Photogrammetry and deep learning [J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(6): 693-704. (in Chinese)
[31] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation [J]. 2015[2023-03-21]. https://arxiv.org/abs/1505.04597.
[32] Girshick R. Fast R-CNN [DB/OL]. 2015[2023-03-21]. https://arxiv.org/abs/1504.08083.
[33] Girshick R, Donahue J, Darrell T, et al. Region-based convolutional networks for accurate object detection and segmentation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(1): 142-158.
[34] 江一帆, 于海洋, 李朝亮, 等. 基于改进Faster R-CNN的高分遥感影像储油罐检测与信息提取[J]. 遥感信息, 2020, 35(4): 89-96. Jiang Y F, Yu H Y, Li C L, et al. Oil storage tank detection and information extraction from high-resolution remote sensing imagery based on improved Faster R-CNN [J]. Remote Sensing Information, 2020, 35(4): 89-96. (in Chinese)
[35] 林娜, 冯丽蓉, 张小青. 基于优化Faster R-CNN的遥感影像飞机检测[J]. 遥感技术与应用, 2021, 36(2): 275-284. Lin N, Feng L R, Zhang X Q. Aircraft detection in remote sensing image based on optimized Faster R-CNN [J]. Remote Sensing Technology and Application, 2021, 36(2): 275-284.(in Chinese)
[36] Ren S Q, He K M, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks [DB/OL]. 2015[2023-03-21]. https://arxiv.org/abs/1506.01497.
[37] Redmon J, Divvala S, Girshick R, et al. You only look once: unified, real-time object detection [DB/OL]. 2015[2023-03-21]. https://arxiv.org/abs/1506.02640.
[38] He K M, Gkioxari G, Dollár P, et al. Mask R-CNN [C]//IEEE International Conference on Computer Vision (ICCV), 2017: 2961-2969.
[39] 彭宝钗, 郑成. 基于深度学习的建筑物检测方法[J]. 电脑编程技巧与维护, 2022(2): 136-138. Peng B C, Zheng C. Building detection method based on deep learning [J]. Computer Programming Skills & Maintenance, 2022(2): 136-138. (in Chinese)
[40] Lu T T, Ming D P, Lin X G, et al. Detecting building edges from high spatial resolution remote sensing imagery using richer convolution features network [J]. Remote Sensing, 2018, 10(9): 1496-1500.
[41] 左俊皓, 赵聪, 朱晓龙, 等. Faster-RCNN和Level-Set结合的高分遥感影像建筑物提取[J]. 液晶与显示, 2019, 34(4): 439-447. Zuo J H, Zhao C, Zhu X L, et al. High-resolution remote sensing image building extraction combined with faster-RCNN and level-set [J]. Chinese Journal of Liquid Crystals and Displays, 2019, 34(4): 439-447.(in Chinese)
[42] Sun G, Huang H, Weng Q, et al. Combinational shadow index for building shadow extraction in urban areas from Sentinel-2A MSI imagery [J]. International Journal of Applied Earth Observation and Geoinformation, 2019, 78(2): 53-65.
[43] Garcia-Garcia A, Orts-Escolano S, Oprea S, et al. A review on deep learning techniques applied to semantic segmentation [DB/OL]. 2017[2023-03-21]. https://arxiv.org/abs/1704.06857.
[44] 胡敏, 况润元, 王利花, 等. 基于BP神经网络的SAR海表风场反演: 以长江入海口及临近海域为例 [J]. 海洋湖沼通报, 2020, 35(2): 35-42. Hu M, Kuang R Y, Wang L H, et al. Retrieval of sea surface wind field of SAR based on BP neural network-taking the Yangtze River estuary with its adjacent seas as an example [J]. Transactions of Oceanology and Limnology, 2020, 35(2): 35-42. (in Chinese)
[45] 刘亦凡, 张秋昭, 王光辉, 等. 利用深度残差网络的遥感影像建筑物提取[J]. 遥感信息, 2020, 35(2): 59-64. Liu Y F, Zhang Q Z, Wang G H, et al. Building extraction in remote sensing imagery based on deep residual network [J]. Remote Sensing Information, 2020, 35(2): 59-64. (in Chinese)
[46] 李志强. 基于深度学习的城市建筑物提取方法研究[D]. 北京: 北京建筑大学, 2019.
[47] 宋师然. 高分遥感城市建筑物对象化识别方法研究[D]. 北京: 北京建筑大学, 2020.
[48] 胡舒, 王树根, 王越, 等. 基于Mask R-CNN的高分遥感影像建筑物目标检测算法[J]. 测绘地理信息, 2023, 48(3): 50-54. Hu S, Wang S G, Wang Y, et al. Building object detection in high-resolution remote sensing image based on Mask R-CNN [J]. Journal of Geomatics, 2023, 48(3): 50-54. (in Chinese)
[49] 季顺平, 魏世清. 遥感影像建筑物提取的卷积神经元网络与开源数据集方法[J]. 测绘学报, 2019, 48(4): 448-459. Ji S P, Wei S Q. Building extraction via convolutional neural networks from an open remote sensing building dataset [J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(4): 448-459. (in Chinese)
[50] 吴樊, 张红， 王超， 等. SARBuD 1.0: 面向深度学习的GF-3精细模式SAR建筑数据集[J]. 遥感学报, 2022, 26(4): 620-631. Wu F, Zhang H, Wang C, et al. SARBuD1.0: a SAR building dataset based on GF-3 FSII imageries for built-up area extraction with deep learning method [J]. National Remote Sensing Bulletin, 2022, 26(4): 620-631. (in Chinese)
[51] 龚健雅, 许越, 胡翔云, 等. 遥感影像智能解译样本库现状与研究[J]. 测绘学报, 2021, 50(8): 1013- 1022. Gong J Y, Xu Y, Hu X Y, et al. Status analysis and research of sample database for intelligent interpretation of remote sensing image [J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8): 1013-1022. (in Chinese)
[52] Zhu X X, Tuia D, Mou L C, et al. Deep learning in remote sensing: a comprehensive review and list of resources [J]. IEEE Geoscience and Remote Sensing Magazine, 2017, 5(4): 8-36.
[53] Long Y, Xia G S, Li S, et al. On creating benchmark dataset for aerial image interpretation: reviews, guidances and million-AID [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 4205-4230.
