基于深度学习的遥感图像水体提取综述

doi:10.6046/zrzyyg.2023106

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摘要

对江河湖泊等水体目标的空间分布、时序变化进行及时、准确的检测和统计具有十分重要的意义和应用价值,已成为当前遥感地表观测研究的重要热点。传统水体提取方法依靠经验设计的指数模型进行水体阈值分割或分类,易受到植被、建筑物等地物的阴影以及水体自身泥沙含量、盐碱浓度等理化特性变化的影响,难以在不同时空尺度环境下保持鲁棒性。随着海量多源、多分辨率的遥感图像的快速获取,深度学习算法在水体提取方面的优势逐渐凸显并被国内外学者广泛关注。得益于深度神经网络模型强大的学习能力和灵活的卷积结构设计方案,研究人员陆续提出了各种模型和学习策略用以提高水体提取的鲁棒性和精度,但目前缺少对该类研究进展的全面综述和问题剖析。因此,文章对近年来国内外发表的相关研究成果进行总结,重点归纳不同算法在遥感图像水体提取方面的优缺点及存在的问题,并对基于深度学习的遥感图像水体提取方法研究的发展提出了建议和展望。

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	温泉
	李璐
	熊立
	杜磊
	刘庆杰
	温奇

关键词 ：水体提取, 遥感图像, 多模态数据, 学习算法, 深度学习

Abstract：

Timely and accurate detection and statistical analysis of the spatial distributions and time-series variations of water bodies like rivers and lakes holds critical significance and application value. It has become a significant interest in current remote sensing surface observation research. Conventional water body extraction methods rely on empirically designed index models for threshold-based segmentation or classification of water bodies. They are susceptible to shadows of surface features like vegetation and buildings, and physicochemical characteristics like sediment content and saline-alkali concentration in water bodies, thus failing to maintain robustness under different spatio-temporal scales. With the rapid acquisition of massive multi-source and multi-resolution remote sensing images, deep learning algorithms have gradually exhibited prominent advantages in water body extraction, garnering considerable attention both domestically and internationally. Thanks to the powerful learning abilities and flexible convolutional structure design schemes of deep neural network models, researchers have successively proposed various models and learning strategies to enhance the robustness and accuracy of water body extraction. However, there lacks a comprehensive review and problem analysis of research advances in this regard. Therefore, this study summarized the relevant research results published domestically and internationally in recent years, especially the advantages, limitations, and existing problems of different algorithms in the water body extraction from remote sensing images. Moreover, this study proposed suggestions and prospects for the advancement of deep learning-based methods for extracting water bodies from remote sensing images.

Key words： water body extraction remote sensing image multimodal data learning algorithm deep learning

收稿日期: 2023-04-18 出版日期: 2024-09-03

ZTFLH:

TP753

基金资助:国家自然科学基金项目“基于深度学习的高分辨率遥感影像建筑物检测与实例分割研究”(41871283)

通讯作者: 熊立(1981-),男,硕士,工程师,主要从事灾害应急管理与灾情评估领域的研究。Email: 49307522@qq.com。

作者简介: 温泉(1985-),男,硕士,工程师,主要从事计算机视觉、自然语言处理领域的研究。Email: aristoego@gmail.com。

引用本文:

温泉, 李璐, 熊立, 杜磊, 刘庆杰, 温奇. 基于深度学习的遥感图像水体提取综述[J]. 自然资源遥感, 2024, 36(3): 57-71.
WEN Quan, LI Lu, XIONG Li, DU Lei, LIU Qingjie, WEN Qi. A review of water body extraction from remote sensing images based on deep learning. Remote Sensing for Natural Resources, 2024, 36(3): 57-71.

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https://www.gtzyyg.com/CN/10.6046/zrzyyg.2023106 或 https://www.gtzyyg.com/CN/Y2024/V36/I3/57

Fig.1 不同传感器和空间分辨率遥感图像中的水体表征

Fig.2 FCN和U-Net的设计思想和网络架构

Fig.3 基于深度学习的水体提取流程

Fig.4 遥感图像水体特征提取方法及模型结构

[1]	Fan Y D, Wen Q, Wang W, et al. Quantifying disaster physical damage using remote sensing data:A technical work flow and case study of the 2014 Ludian earthquake in China[J]. International Journal of Disaster Risk Science, 2017, 8(4):471-488.
[2]	王志豪, 李刚, 蒋骁. 基于光学和SAR遥感图像融合的洪灾区域检测方法[J]. 雷达学报, 2020, 9(3):539-553.
	Wang Z H, Li G, Jiang X. Flooded area detection method based on fusion of optical and SAR remote sensing images[J]. Journal of Radars, 2020, 9(3):539-553.
[3]	李丹, 吴保生, 陈博伟, 等. 基于卫星遥感的水体信息提取研究进展与展望[J]. 清华大学学报(自然科学版), 2020, 60(2):147-161.
	Li D, Wu B S, Chen B W, et al. Review of water body information extraction based on satellite remote sensing[J]. Journal of Tsinghua University(Science and Technology), 2020, 60(2):147-161.
[4]	苏龙飞, 李振轩, 高飞, 等. 遥感影像水体提取研究综述[J]. 国土资源遥感, 2021, 33(1):9-19.doi:10.6046/gtzyyg.2020170.
	Su L F, Li Z X, Gao F, et al. A review of remote sensing image water extraction[J]. Remote Sensing for Land and Resources, 2021, 33(1):9-19.doi:10.6046/gtzyyg.2020170.
[5]	谭衢霖, 刘正军, 胡吉平, 等. 应用多源遥感影像提取鄱阳湖形态参数[J]. 北京交通大学学报, 2006, 30(4):26-30.
	Tan Q L, Liu Z J, Hu J P, et al. Measuring lake water level using multi-source remote sensing images combined with hydrological statistical data[J]. Journal of Beijing Jiaotong University, 2006, 30(4):26-30.
[6]	孙德勇, 李云梅, 王桥, 等. 不同浓度悬浮泥沙水体的光谱吸收特性模拟[J]. 地理与地理信息科学, 2008, 24(5):16-20.
	Sun D Y, Li Y M, Wang Q, et al. Simulation of spectral absorption characteristics of suspended sediment water[J]. Geography and Geo-Information Science, 2008, 24(5):16-20.
[7]	张鹰, 张东, 王艳姣, 等. 含沙水体水深遥感方法的研究[J]. 海洋学报, 2008, 30(1):51-58.
	Zhang Y, Zhang D, Wang Y J, et al. Study of remote sensing-based bathymetric method in sand-containing water bodies[J]. Acta Oceanologica Sinica, 2008, 30(1):51-58.
[8]	李红, 刘芳, 杨淑媛, 等. 基于深度支撑值学习网络的遥感图像融合[J]. 计算机学报, 2016, 39(8):1583-1596.
	Li H, Liu F, Yang S Y, et al. Remote sensing image fusion based on deep support value learning networks[J]. Chinese Journal of Computers, 2016, 39(8):1583-1596.
[9]	徐俊峰, 张保明, 余东行, 等. 多特征融合的高分辨率遥感影像飞机目标变化检测[J]. 遥感学报, 2020, 24(1):37-52.
	Xu J F, Zhang B M, Yu D H, et al. Aircraft target change detection for high-resolution remote sensing images using multi-feature fusion[J]. Journal of Remote Sensing, 2020, 24(1):37-52.
[10]	Li L, Wang C, Zhang H, et al. Urban building change detection in SAR images using combined differential image and residual U-Net network[J]. Remote Sensing, 2019, 11(9):1091.
[11]	Ruiz H D, Bacca C B, Caicedo B E. Data classification of hyperspectral images based on inception networks and extended attribute profiles[J]. International Journal of Remote Sensing, 2020, 41(22):8717-8738.
[12]	王航, 秦奋. 遥感影像水体提取研究综述[J]. 测绘科学, 2018, 43(5):23-32.
	Wang H, Qin F. Summary of the research on water body extraction and application from remote sensing image[J]. Science of Surveying and Mapping, 2018, 43(5):23-32.
[13]	袁欣智, 江洪, 陈芸芝, 等. 一种应用大津法的自适应阈值水体提取方法[J]. 遥感信息, 2016, 31(5):36-42.
	Yuan X Z, Jiang H, Chen Y Z, et al. Extraction of water body information using adaptive threshold value and OTSU algorithm[J]. Remote Sensing Information, 2016, 31(5):36-42.
[14]	曾玲方, 李霖, 万丽华. 基于Sentinel-1卫星SAR数据的洪水淹没范围快速提取[J]. 地理信息世界, 2015, 22(5):100-103,107.
	Zeng L F, Li L, Wan L H. SAR based fast flood mapping using Sentinel-1 imagery[J]. Geomatics World, 2015, 22(5):100-103,107.
[15]	Cao H, Zhang H, Wang C, et al. Operational flood detection using Sentinel-1 SAR data over large areas[J]. Water, 2019, 11(4):786.
[16]	Klemenjak S, Waske B, Valero S, et al. Automatic detection of ri-vers in high-resolution SAR data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012, 5(5):1364-1372.
[17]	Martinis S, Twele A, Voigt S. Unsupervised extraction of flood-induced backscatter changes in SAR data using Markov image modeling on irregular graphs[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(1):251-263.
[18]	McFeeters S K. The use of the normalized difference water index (NDWI) in the delineation of open water features[J]. International Journal of Remote Sensing, 1996, 17(7):1425-1432.
[19]	都金康, 黄永胜, 冯学智, 等. SPOT卫星影像的水体提取方法及分类研究[J]. 遥感学报, 2001, 5(3):214-219.
	Du J K, Huang Y S, Feng X Z, et al. Study on water bodies extraction and classification from SPOT image[J]. Journal of Remote Sensing, 2001, 5(3):214-219.
[20]	徐涵秋. 利用改进的归一化差异水体指数(MNDWI)提取水体信息的研究[J]. 遥感学报, 2005, 9(5):589-595.
	Xu H Q. A study on information extraction of water body with the modified normalized difference water index(MNDWI)[J]. Journal of Remote Sensing, 2005, 9(5):589-595.
[21]	骆剑承, 盛永伟, 沈占锋, 等. 分步迭代的多光谱遥感水体信息高精度自动提取[J]. 遥感学报, 2009, 13(4):610-615.
	Luo J C, Sheng Y W, Shen Z F, et al. Automatic and high-precise extraction for water information from multispectral images with the step-by-step iterative transformation mechanism[J]. Journal of Remote Sensing, 2009, 13(4):610-615.
[22]	沈占锋, 夏列钢, 李均力, 等. 采用高斯归一化水体指数实现遥感影像河流的精确提取[J]. 中国图象图形学报, 2013, 18(4):421-428.
	Shen Z F, Xia L G, Li J L, et al. Automatic and high-precision extraction of rivers from remotely sensed images with Gaussian normalized water index[J]. Journal of Image and Graphics, 2013, 18(4):421-428.
[23]	Yamazaki D, Trigg M A, Ikeshima D. Development of a global -90 m water body map using multi-temporal Landsat images[J]. Remote Sensing of Environment, 2015, 171:337-351.
[24]	Guo Q D, Pu R L, Li J L, et al. A weighted normalized difference water index for water extraction using Landsat imagery[J]. International Journal of Remote Sensing, 2017, 38(19):5430-5445.
[25]	Zhou Y, Zhao H R, Hao H, et al. A new multi-spectral threshold normalized difference water index (MST-NDWI) water extraction method:A case study in Yanhe watershed[J]. The International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences,2018,XLII-3:2557-2564.
[26]	Yao F F, Wang J D, Wang C, et al. Constructing long-term high-frequency time series of global lake and reservoir areas using Landsat imagery[J]. Remote Sensing of Environment, 2019, 232:111210.
[27]	Ahmad S K, Hossain F, Eldardiry H, et al. A fusion approach for water area classification using visible,near infrared and synthetic aperture Radar for South Asian conditions[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(4):2471-2480.
[28]	Feyisa G L, Meilby H, Fensholt R, et al. Automated water extraction index:A new technique for surface water mapping using Landsat imagery[J]. Remote Sensing of Environment, 2014, 140:23-35.
[29]	Yang X C, Qin Q M, Grussenmeyer P, et al. Urban surface water body detection with suppressed built-up noise based on water indices from Sentinel-2 MSI imagery[J]. Remote Sensing of Environment, 2018, 219:259-270.
[30]	郭建聪, 李培军, 肖晓柏. 一种高分辨率多光谱图像的多尺度分割方法[J]. 北京大学学报(自然科学版), 2009, 45(2):306-310.
	Guo J C, Li P J, Xiao X B. A hierarchical segmentation method for multispectral imagery[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2009, 45(2):306-310.
[31]	Tehrany M S, Pradhan B, Jebur M N. Remote sensing data reveals eco-environmental changes in urban areas of Klang Valley,Malaysia:Contribution from object based analysis[J]. Indian Society of Remote Sensing, 2013, 41(4):981-991.
[32]	张猛, 曾永年, 朱永森. 面向对象方法的时间序列MODIS数据湿地信息提取——以洞庭湖流域为例[J]. 遥感学报, 2017, 21(3):479-492.
	Zhang M, Zeng Y N, Zhu Y S. Wetland mapping of Donting Lake basin based on time-series MODIS data and object-oriented method[J]. Journal of Remote Sensing, 2017, 21(3):479-492.
[33]	Deng Y, Zhang H, Wang C, et al. Object-oriented water extraction of PolSAR image based on target decomposition[C]// 2015 IEEE 5th Asia-Pacific Conference on Synthetic Aperture Radar(APSAR),IEEE, 2015: 596-601.
[34]	邓滢, 张红, 王超, 等. 结合纹理与极化分解的面向对象极化SAR水体提取方法[J]. 遥感技术与应用, 2016, 31(4):714-723. doi: 10.11873/j.issn.1004-0323.2016.4.0714
	Deng Y, Zhang H, Wang C, et al. An object-oriented water extraction method based on texture and polarimetric decomposition feature[J]. Remote Sensing Technology and Application, 2016, 31(4):714-723.
[35]	吴瑞娟, 何秀凤. 高分雷达与光学影像融合的滨海湿地变化检测[J]. 测绘科学, 2020, 45(11):93-100.
	Wu R J, He X F. Coastal wetland change detection using fusion of high resolution Radar and optical images[J]. Science of Surveying and Mapping, 2020, 45(11):93-100.
[36]	林顺海. 基于NDWI与改进型FCM相结合的高分一号影像水域信息提取方法研究[J]. 测绘与空间地理信息, 2017, 40(6):86-88.
	Lin S H. Study on water information extraction method of GF-1 image based on the combination of NDWI and modified FCM[J]. Geomatics and Spatial Information Technology, 2017, 40(6):86-88.
[37]	洪亮, 黄雅君, 杨昆, 等. 复杂环境下高分二号遥感影像的城市地表水体提取[J]. 遥感学报, 2019, 23(5):871-882.
	Hong L, Huang Y J, Yang K, et al. Study on urban surface water extraction from heterogeneous environments using GF-2 remotely sensed images[J]. Journal of Remote Sensing, 2019, 23(5):871-882.
[38]	段秋亚, 孟令奎, 樊志伟, 等. GF-1卫星影像水体信息提取方法的适用性研究[J]. 国土资源遥感, 2015, 27(4):79-84.doi:10.6046/gtzyyg.2015.04.13.
	Duan Q Y, Meng L K, Fan Z W, et al. Applicability of the water information extraction method based on GF-1 image[J]. Remote Sensing for Land and Resources, 2015, 27(4):79-84.doi:10.6046/gtzyyg.2015.04.13.
[39]	慎利, 唐宏, 王世东, 等. 结合空间像素模板和Adaboost算法的高分辨率遥感影像河流提取[J]. 测绘学报, 2013, 42(3):344-350.
	Shen L, Tang H, Wang S D, et al. River extraction from the high resolution remote sensing image based on spatially correlated pixels template and Adaboost algorithm[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(3):344-350.
[40]	宋英强, 杨联安, 许婧婷, 等. 基于Landsat-8卫星OLI遥感影像和AdaBoost算法的水体信息提取[J]. 测绘地理信息, 2017, 42(3):44-47.
	Song Y Q, Yang L A, Xu J T, et al. Water body information extraction based on Landsat-8 satellite OLI remote sensing image and AdaBoost algorithm[J]. Journal of Geomatics, 2017, 42(3):44-47.
[41]	汪权方, 张梦茹, 张雨, 等. 基于视觉注意机制的大范围水体信息遥感智能提取[J]. 计算机应用, 2020, 40(4):1038-1044. doi: 10.11772/j.issn.1001-9081.2019081492
	Wang Q F, Zhang M R, Zhang Y, et al. Intelligent extraction of remote sensing information on large-scale water based on visual attention mechanism[J]. Journal of Computer Applications, 2020, 40(4):1038-1044. doi: 10.11772/j.issn.1001-9081.2019081492
[42]	胡德勇, 李京, 陈云浩, 等. 单波段单极化SAR图像水体和居民地信息提取方法研究[J]. 中国图象图形学报, 2008, 13(2):257-263.
	Hu D Y, Li J, Chen Y H, et al. Water and settlement area extraction from single-band,single-polarization SAR images based on SVM method[J]. Journal of Image and Graphics, 2008, 13(2):257-263.
[43]	Pradhan B, Hagemann U, Shafapour Tehrany M, et al. An easy to use ArcMap based texture analysis program for extraction of flooded areas from TerraSAR-X satellite image[J]. Computers and Geosciences, 2014, 63:34-43.
[44]	Goodfellow I J, Pouget-Abadie J, Mirza M, et al. Generative adversarial networks[J/OL]. arXiv, 2014(2014-06-10). https://arxiv.org/abs/1406.2661v1.
[45]	Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J/OL]. arxiv, 2014(2014-09-04). https://arxiv.org/abs/1409.1556.
[46]	He K M, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition CVPR.IEEE. 2016:770-778.
[47]	Tan M X, Le Q V. EfficientNet:Rethinking model scaling for convolutional neural networks[J/OL]. arXiv, 2019(2019-05-28). http://arxiv.org/abs/1905.11946v5.
[48]	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. doi: 10.1109/TPAMI.2016.2572683 pmid: 27244717
[49]	Ronneberger O, Fischer P, Brox T. U-net:Convolutional networks for biomedical image segmentation[C]// International Conference on Medical Image Computing and Computer-Assisted Intervention.Cham:Springer, 2015:234-241.
[50]	Chen L C, Papandreou G, Kokkinos I, et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs[J/OL]. arXiv, 2014(2014-12-22). https://arxiv.org/abs/1412.7062v4.
[51]	Chen L C, Papandreou G, Schroff F, et al. Rethinking atrous convolution for semantic image segmentation[J/OL]. arXiv, 2017(2017-06-17). https://arxiv.org/abs/1706.05587.
[52]	Gong M G, Yang H L, Zhang P Z. Feature learning and change feature classification based on deep learning for ternary change detection in SAR images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 129:212-225.
[53]	李鑫伟, 李彦胜, 张永军. 弱监督深度语义分割网络的多源遥感影像水体检测[J]. 中国图象图形学报, 2021, 26(12):3015-3026.
	Li X W, Li Y S, Zhang Y J. Weakly supervised deep semantic segmentation network for water body extraction based on multi-source remote sensing imagery[J]. Journal of Image and Graphics, 2021, 26(12):3015-3026.
[54]	王知音, 禹龙, 田生伟, 等. 基于栈式自编码的水体提取方法[J]. 计算机应用, 2015, 35(9):2706-2709. doi: 10.11772/j.issn.1001-9081.2015.09.2706
	Wang Z Y, Yu L, Tian S W, et al. Water body extraction method based on stacked autoencoder[J]. Journal of Computer Applications, 2015, 35(9):2706-2709. doi: 10.11772/j.issn.1001-9081.2015.09.2706
[55]	Guo Y H, Wang S, Gao C Q, et al. Wishart RBM based DBN for polarimetric synthetic Radar data classification[C]// 2015 IEEE International Geoscience and Remote Sensing Symposium,(IGARSS).IEEE, 2015: 1841-1844.
[56]	Liu F, Jiao L C, Hou B, et al. Pol-SAR image classification based on wishart DBN and local spatial information[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(6):3292-3308.
[57]	杜敬. 基于深度学习的无人机遥感影像水体识别[J]. 江西科学, 2017, 35(1):158-161,170.
	Du J. Deep learning based UAV remote sensing image water body identification[J]. Jiangxi Science, 2017, 35(1):158-161,170.
[58]	何海清, 杜敬, 陈婷, 等. 结合水体指数与卷积神经网络的遥感水体提取[J]. 遥感信息, 2017, 32(5):82-86.
	He H Q, Du J, Chen T, et al. Remote sensing image water body extraction combing NDWI with convolutional neural network[J]. Remote Sensing Information, 2017, 32(5):82-86.
[59]	梁泽毓. 基于深度学习的多源遥感水体信息提取方法及其应用研究[D]. 合肥: 安徽大学, 2019.
	Liang Z Y. Research on water information extraction method of multi-source remote sensing based on deep learning and its application[D]. Hefei: Anhui University, 2019.
[60]	吕亚龙, 田生伟, 禹龙, 等. 空谱联合特征的CNN_SVM水体识别[J]. 计算机工程与设计, 2019, 40(5):1435-1439.
	Lyu Y L, Tian S W, Yu L, et al. CNN_SVM water body recognition based on spatial-spectral features[J]. Computer Engineering and Design, 2019, 40(5):1435-1439.
[61]	Wang Y D, Li Z W, Zeng C, et al. An urban water extraction method combining deep learning and Google Earth Engine[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13:769-782.
[62]	Wang L, Wang H. Water hazard detection using conditional generative adversarial network with mixture reflection attention units[J]. IEEE Access, 2019, 7:167497-167506. doi: 10.1109/ACCESS.2019.2953768
[63]	王惠英, 孙中平, 孙志伟, 等. 基于深度学习的河道提取与变化监测应用——以永定河为例[J]. 北京测绘, 2019, 33(2):173-178.
	Wang H Y, Sun Z P, Sun Z W, et al. The application for extraction of river channels and change detection based on deep learning:A case study from Yongding River[J]. Beijing Surveying and Mapping, 2019, 33(2):173-178.
[64]	Weng L G, Xu Y M, Xia M, et al. Water areas segmentation from remote sensing images using a separable residual SegNet network[J]. ISPRS International Journal of Geo-Information, 2020, 9(4):256.
[65]	Zhang Z X, Liu Q J, Wang Y H. Road extraction by deep residual U-Net[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(5):749-753.
[66]	吴从中, 陈曦, 季栋, 等. 结合深度残差学习和感知损失的图像去噪[J]. 中国图象图形学报, 2018, 23(10):1483-1491.
	Wu C Z, Chen X, Ji D, et al. Image denoising via residual network based on perceptual loss[J]. Journal of Image and Graphics, 2018, 23(10):1483-1491.
[67]	Qiu C P, Mou L C, Schmitt M, et al. Fusing multiseasonal Sentinel-2 imagery for urban land cover classification with multibranch residual convolutional neural networks[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 17(10):1787-1791.
[68]	Cai Y H, Guo Y J, Lang S N, et al. Classification of hyperspectral images by spectral-spatial dense-residual network[J]. Journal of Applied Remote Sensing, 2020, 14(3):036513.
[69]	Yu L, Zhang R N, Tian S W, et al. Deep multi-feature learning for water body extraction from Landsat imagery[J]. Automatic Control and Computer Sciences, 2018, 52(6):517-527.
[70]	赵海萍. 在大数据下联合空谱特征与深度学习的水体识别研究[D]. 大连: 大连交通大学, 2018.
	Zhao H P. Research on water identification with hollow-spectrum features and deep learning based on big data[D]. Dalian: Dalian Jiaotong University, 2018.
[71]	Chen Y, Tang L L, Kan Z H, et al. A novel water body extraction neural network (WBE-NN) for optical high resolution multispectral imagery[J]. Journal of Hydrology, 2020, 588:125092.
[72]	Miao Z M, Fu K, Sun H, et al. Automatic water body segmentation from high-resolution satellite images via deep networks[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(4):602-606.
[73]	Talal M, Panthakkan A, Mukhtar H, et al. Detection of water bodies using semantic segmentation[C]// 2018 International Conference on Signal Processing and Information Security(ICSPIS).IEEE, 2018: 1-4.
[74]	杨知, 欧文浩, 刘晓燕, 等. 基于LinkNet卷积神经网络的高分辨率遥感影像水体信息提取[J]. 云南大学学报(自然科学版), 2019, 41(5):932-938.
	Yang Z, Ou W H, Liu X Y, et al. Water information extraction for high resolution remote sensing image based on LinkNet convolutional neural network[J]. Journal of Yunnan University(Natural Sciences Edition), 2019, 41(5):932-938.
[75]	Yan R D, Dong S. Optical remote sensing image waters extraction technology based on deep learning context-Unet[C]// 2019 IEEE International Conference on Signal,Information and Data Processing(ICSIDP).IEEE, 2019: 1-4.
[76]	Nemni E, Bullock J, Belabbes S, et al. Fully convolutional neural network for rapid flood segmentation in synthetic aperture Radar imagery[J]. Remote Sensing, 2020, 12(16):2532.
[77]	王雪, 隋立春, 钟棉卿, 等. 全卷积神经网络用于遥感影像水体提取[J]. 测绘通报, 2018(6):41-45. doi: 10.13474/j.cnki.11-2246.2018.0173
	Wang X, Sui L C, Zhong M Q, et al. Fully convolution neural networks for water extraction of remote sensing images[J]. Bulletin of Surveying and Mapping, 2018(6):41-45. doi: 10.13474/j.cnki.11-2246.2018.0173
[78]	Kim S M, Shin J, Baek S, et al. U-Net convolutional neural network model for deep red tide learning using GOCI[J]. Journal of Coastal Research, 2019, 90(s1):302.
[79]	王宁, 程家骅, 张寒野, 等. U-net模型在高分辨率遥感影像水体提取中的应用[J]. 国土资源遥感, 2020, 32(1):35-42.doi:10.6046/gtzyyg.2020.01.06.
	Wang N, Cheng J H, Zhang H Y, et al. Application of U-net model to water extraction with high resolution remote sensing data[J]. Remote Sensing for Land and Resources, 2020, 32(1):35-42.doi:10.6046/gtzyyg.2020.01.06.
[80]	Ge C J, Xie W J, Meng L K. Extracting lakes and reservoirs from GF-1 satellite imagery over China using improved U-Net[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19:1504105.
[81]	Li Z Y, Wang R, Zhang W, et al. Multiscale features supported DeepLab V3+ optimization scheme for accurate water semantic segmentation[J]. IEEE Access, 2019, 7:155787-155804.
[82]	Duan L H, Hu X Y. Multiscale refinement network for water body segmentation in high-resolution satellite imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 17(4):686-690.
[83]	Feng W Q, Sui H G, Huang W M, et al. Water body extraction from very high-resolution remote sensing imagery using deep U-Net and a superpixel-based conditional random field model[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(4):618-622.
[84]	Chen Y, Fan R S, Yang X C, et al. Extraction of urban water bodies from high-resolution remote-sensing imagery using deep learning[J]. Water, 2018, 10(5):585.
[85]	沈骏翱, 马梦婷, 宋致远, 等. 基于深度学习语义分割模型的高分辨率遥感图像水体提取[J]. 自然资源遥感, 2022, 34(4):129-135.doi:10.6046/zrzyyg.2021357.
	Shen J A, Ma M T, Song Z Y, et al. Water information extraction from high-resolution remote sensing images using the deep-learning based semantic segmentation model[J]. Remote Sensing for Natural Resources, 2022, 34(4):129-135.doi:10.6046/zrzyyg.2021357.
[86]	Wieland M, Martinis S, Kiefl R, et al. Semantic segmentation of water bodies in very high-resolution satellite and aerial images[J]. Remote Sensing of Environment, 2023, 287:113452.
[87]	Parajuli J, Fernandez-Beltran R, Kang J, et al. Attentional dense convolutional neural network for water body extraction from Sentinel-2 images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15:6804-6816.
[88]	Lu M, Fang L, Li M, et al. NFANet:A novel method for weakly supervised water extraction from high-resolution remote-sensing imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60:5617114.
[89]	Xiang D Q, Zhang X, Wu W, et al. DensePPMUNet:A robust deep learning network for segmenting water bodies from aerial images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61:4202611.
[90]	Zhang P P, Wang G J. The modified encoder-decoder network based on depthwise separable convolution for water segmentation of real SAR imagery[C]// International Applied Computational Electromagnetics Society Symposium-China(ACES).IEEE, 2019:1-2.
[91]	Zhang J S, Xing M D, Sun G C, et al. Water body detection in high-resolution SAR images with cascaded fully-convolutional network and variable focal loss[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(1):316-332.
[92]	Zhang P, Chen L F, Li Z H, et al. Automatic extraction of water and shadow from SAR images based on a multi-resolution dense encoder and decoder network[J]. Sensors, 2019, 19(16):3576.
[93]	Avolio C, Tricomi A, Mammone C, et al. A deep learning architecture for heterogeneous and irregularly sampled remote sensing time series[C]// 2019 IEEE International Geoscience and Remote Sensing Symposium,Yokohama(IGARSS).IEEE, 2019:9807-9810.
[94]	Huang K, Nie W, Luo N X. Fully polarized SAR imagery classification based on deep reinforcement learning method using multiple polarimetric features[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(10):3719-3730. doi: 10.1109/JSTARS.2019.2913445
[95]	Ahishali M, Kiranyaz S, Ince T, et al. Multifrequency PolSAR image classification using dual-band 1D convolutional neural networks[C]// 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS).IEEE, 2020:73-76.
[96]	Ni J, Zhang F, Yin Q, et al. Random neighbor pixel-block-based deep recurrent learning for polarimetric SAR image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(9):7557-7569.
[97]	Wang Y, He C, Liu X, et al. A hierarchical fully convolutional network integrated with sparse and low-rank subspace representations for PolSAR imagery classiﬁcation[J]. Remote Sensing, 2018, 10(2):342.
[98]	Xue W B, Yang H, Wu Y L, et al. Water body automated extraction in polarization SAR images with dense-coordinate-feature-concatenate network[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14:12073-12087.
[99]	Hertel V, Chow C, Wani O, et al. Probabilistic SAR-based water segmentation with adapted Bayesian convolutional neural network[J]. Remote Sensing of Environment, 2023, 285:113388.
[100]	Zhang Z M, Wang H P, Xu F, et al. Complex-valued convolutional neural network and its application in polarimetric SAR image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(12):7177-7188.
[101]	Chen J K, Qiu X L. Equivalent complex valued deep semantic segmentation network for SAR images[C]// 2019 International Applied Computational Electromagnetics Society Symposium-China(ACES).IEEE, 2019:1-2.
[102]	Wilmanski M, Kreucher C, Hero A. Complex input convolutional neural networks for wide angle SAR ATR[C]// 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP).IEEE, 2016:1037-1041.
[103]	Sun Z B, Xu X H, Pan Z. SAR ATR using complex-valued CNN[C]// 2020 Asia-Pacific Conference on Image Processing,Electronics and Computers (IPEC).IEEE, 2020:125-128.
[104]	Mu H L, Zhang Y, Jiang Y C, et al. CV-GMTINet:GMTI using a deep complex-valued convolutional neural network for multichannel SAR-GMTI system[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60:5201115.
[105]	李胜. 联合领域知识和深度学习的城市地表覆盖变化检测方法[D]. 武汉: 武汉大学, 2018.
	Li S. Detection method of urban land cover change based on combining domain knowledge and deep learning[D]. Wu Han: Wuhan University, 2018.
[106]	张书瑜. 基于深度学习和多尺度多特征融合的高分辨率遥感地表覆盖分类研究[D]. 杭州: 浙江大学, 2020.
	Zhang S Y. High resolution remote sensing image land cover classification based on deep learning and multi-scale and multi-feature fusion[D]. Hangzhou: Zhejiang University, 2020.
[107]	Sun H, Liu S, Zhou S, et al. Unsupervised cross-view semantic transfer for remote sensing image classification[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(1):13-17.
[108]	Maggiori E, Tarabalka Y, Charpiat G, et al. Convolutional neural networks for large-scale remote-sensing image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(2):645-657.
[109]	Tong X Y, Xia G S, Lu Q K, et al. Learning transferable deep models for land-use classification with high-resolution remote sensing images[J/OL]. arXiv, 2018(2018-07-16). https://arxiv.org/abs/1807.05713v1.
[110]	Wan L, Liu N, Huo H, et al. Selective convolutional neural networks and cascade classifiers for remote sensing image classification[J]. Remote Sensing Letters, 2017, 8(10):917-926.
[111]	Wang G, Fan B, Xiang S, et al. Aggregating rich hierarchical features for scene classification in remote sensing imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(9):4104-4115.
[112]	Geng J, Ma X R, Zhou X J, et al. Saliency-guided deep neural networks for SAR image change detection[J]. IEEE Transaction on Geoscience and Remote Sensing, 2019, 57(10):7365-7377.
[113]	Song A, Kim Y, Kim Y. Change detection of surface water in remote sensing images based on fully convolutional network[J]. Journal of Coastal Research, 2019, 91(s1):426.
[114]	Li Y Y, Peng C, Chen Y Q, et al. A deep learning method for change detection in synthetic aperture Radar images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(8):5751-5763.
[115]	Su L Z, Cao X. Fuzzy autoencoder for multiple change detection in remote sensing images[J]. Journal of Applied Remote Sensing, 2018, 12(3): 035014.
[116]	Gao F, Wang X, Gao Y H, et al. Sea ice change detection in SAR images based on convolutional-wavelet neural networks[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(8):1240-1244.
[117]	Chen H, Wu C, Du B, et al. Change detection in multisource VHR images via deep Siamese convolutional multiple-layers recurrent neural network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(4):2848-2864.
[118]	Lyu H, Lu H, Mou L C. Learning a transferable change rule from a recurrent neural network for land cover change detection[J]. Remote Sensing, 2016, 8(6):506.
[119]	Demir I, Koperski K, Lindenbaum D, et al. DeepGlobe 2018:A challenge to parse the earth through satellite images[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).IEEE, 2018:172-181.
[120]	Zhang M, Hu X, Zhao L, et al. Learning dual multi-scale manifold ranking for semantic segmentation of high-resolution images[J]. Remote Sensing, 2017, 9(5):500.
[121]	Li Y, Chen L. Land cover classification for high resolution remote sensing images with atrous convolution and BFS[C]// 2019 IEEE 5th International Conference on Computer and Communications (ICCC).IEEE, 2019: 1808-1813.
[122]	Schmitt M, Hughes L H, Qiu C, et al. SEN12MS:A curated dataset of georeferenced multi-spectral Sentinel-1/2 imagery for deep learning and data fusion[J]// ISPRS Annals of the Photogrammetry,Remote Sensing and Spatial Information Sciences, 2019,IV-2/W7:153-160.
[123]	许芬, 孟庆岩, 张琳琳. 2013—2017年海南岛陆域水体遥感提取数据集[J]. 中国科学数据, 2019, 4(2): 57-68.
	Xu F, Meng Q Y, Zhang L L. A dataset of inland water distribution in Hainan Island based on remote sensing during 2013—2017[J]. China Scientific Data, 2019, 4(2): 57-68.
[124]	Bonafilia D, Tellman B, Anderson T, et al. Sen1Floods11:A georeferenced dataset to train and test deep learning flood algorithms for Sentinel-1[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).IEEE, 2020: 835-845.
[125]	Miau S, Hung W H. River flooding forecasting and anomaly detection based on deep learning[J]. IEEE Access, 2020, 8:198384-198402.
[126]	Fang W Z, Wang C G, Chen X, et al. Recognizing global reservoirs from Landsat 8 images:A deep learning approach[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(9):3168-3177.
[127]	Li A S, Chirayath V, Segal-Rozenhaimer M, et al. NASA NeMO-Net’s convolutional neural network:Mapping marine habitats with spectrally heterogeneous remote sensing imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13:5115-5133.
[128]	蒋广鑫. 基于深度学习的青藏高原湖泊面积提取及湖泊变化研究[D]. 西安: 西北大学, 2020.
	Jiang G X. Research on lake area extraction and lake changes in the Tibetan Plateau based on deep learning[D]. Xi’an: Northwest University, 2020.

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