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Extraction of Urban Water Bodies from High-Resolution Remote-Sensing Imagery Using Deep Learning
Chen, Yang1,2; Fan, Rongshuang2; Yang, Xiucheng3; Wang, Jingxue1; Latif, Aamir4
2018-05-01
Source PublicationWATER
ISSN2073-4441
Volume10Issue:5Pages:20
Corresponding AuthorChen, Yang(chenyang1017@126.com)
AbstractAccurate information on urban surface water is important for assessing the role it plays in urban ecosystem services in the context of human survival and climate change. The precise extraction of urban water bodies from images is of great significance for urban planning and socioeconomic development. In this paper, a novel deep-learning architecture is proposed for the extraction of urban water bodies from high-resolution remote sensing (HRRS) imagery. First, an adaptive simple linear iterative clustering algorithm is applied for segmentation of the remote-sensing image into high-quality superpixels. Then, a new convolutional neural network (CNN) architecture is designed that can extract useful high-level features of water bodies from input data in a complex urban background and mark the superpixel as one of two classes: an including water or no-water pixel. Finally, a high-resolution image of water-extracted superpixels is generated. Experimental results show that the proposed method achieved higher accuracy for water extraction from the high-resolution remote-sensing images than traditional approaches, and the average overall accuracy is 99.14%.
Keyworddeep learning convolutional neural networks superpixel urban water bodies high-resolution remote-sensing images
DOI10.3390/w10050585
WOS KeywordCONVOLUTIONAL NEURAL-NETWORKS ; OF-THE-ART ; SCENE CLASSIFICATION ; SLIC SUPERPIXELS ; SATELLITE IMAGES ; INDEX NDWI ; SENTINEL-2 ; MANAGEMENT ; FEATURES ; BODY
Indexed BySCI
Language英语
Funding ProjectNation key R&D Program of China[2016YFC0803100] ; National Natural Science Foundation of China[41101452] ; Doctoral Program Foundation of Institutions of Higher Education of China[20112121120003]
Funding OrganizationNation key R&D Program of China ; National Natural Science Foundation of China ; Doctoral Program Foundation of Institutions of Higher Education of China
WOS Research AreaWater Resources
WOS SubjectWater Resources
WOS IDWOS:000435196700051
PublisherMDPI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/54664
Collection中国科学院地理科学与资源研究所
Corresponding AuthorChen, Yang
Affiliation1.Liaoning Tech Univ, Sch Geomat, Fuxing 123000, Peoples R China
2.Chinese Acad Surveying & Mapping, Beijing 100830, Peoples R China
3.Univ Strasbourg, ICube Lab, F-67000 Strasbourg, France
4.Univ Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 10010, Peoples R China
Recommended Citation
GB/T 7714
Chen, Yang,Fan, Rongshuang,Yang, Xiucheng,et al. Extraction of Urban Water Bodies from High-Resolution Remote-Sensing Imagery Using Deep Learning[J]. WATER,2018,10(5):20.
APA Chen, Yang,Fan, Rongshuang,Yang, Xiucheng,Wang, Jingxue,&Latif, Aamir.(2018).Extraction of Urban Water Bodies from High-Resolution Remote-Sensing Imagery Using Deep Learning.WATER,10(5),20.
MLA Chen, Yang,et al."Extraction of Urban Water Bodies from High-Resolution Remote-Sensing Imagery Using Deep Learning".WATER 10.5(2018):20.
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