{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T12:14:36Z","timestamp":1781612076830,"version":"3.54.5"},"reference-count":56,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,10]],"date-time":"2021-08-10T00:00:00Z","timestamp":1628553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41871323"],"award-info":[{"award-number":["41871323"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Pilot Fund of Frontier Science and Disruptive Technology of the Aerospace Information Research Institute, Chinese Academy of Sciences","award":["E0Z21101"],"award-info":[{"award-number":["E0Z21101"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Obtaining water body images quickly and reliably is important to guide human production activities and study urban change. This paper presents a fast and accurate method to identify water bodies in complex environments based on UAV high-resolution images. First, an improved U-Net (SU-Net) model is proposed in this paper. By increasing the number of connections in the middle layer of the neural network, more image features can be retained through S-shaped circular connections. Second, aiming at the interference of mixed ground objects and dark ground objects on water detection, the fusion of a deep learning network and visual features is investigated. We analyse the influence of a wavelet transform and grey level cooccurrence matrix (GLCM) on water extraction. Using a confusion matrix to evaluate accuracy, the following conclusions are drawn: (1) Compared with existing methods, the SU-Net method achieves a significant improvement in accuracy, and the overall accuracy (OA) is 96.25%. The kappa coefficient (KC) is 0.952. (2) SU-Net combined with the GLCM has a higher accuracy (OA is 97.4%) and robustness in distinguishing mixed and dark objects. Based on this method, a distinct water boundary in urban areas, which provides data for urban water vector mapping, can be obtained.<\/jats:p>","DOI":"10.3390\/rs13163165","type":"journal-article","created":{"date-parts":[[2021,8,10]],"date-time":"2021-08-10T22:40:31Z","timestamp":1628635231000},"page":"3165","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Urban Water Extraction with UAV High-Resolution Remote Sensing Data Based on an Improved U-Net Model"],"prefix":"10.3390","volume":"13","author":[{"given":"Wenning","family":"Li","sequence":"first","affiliation":[{"name":"National Engineering Research Center for Geoinformatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Li","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Geoinformatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianhua","family":"Gong","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Geoinformatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Zhejiang-CAS Application Center for Geoinformatics, Jiaxing 314100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quanlong","family":"Feng","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100193, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jieping","family":"Zhou","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Geoinformatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Sun","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Geoinformatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenhui","family":"Shi","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Geoinformatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weidong","family":"Hu","sequence":"additional","affiliation":[{"name":"Zhejiang-CAS Application Center for Geoinformatics, Jiaxing 314100, China"},{"name":"Jiaxing Supersea Information Technology Co., Ltd., Jiaxing 314100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,10]]},"reference":[{"key":"ref_1","first-page":"54","article-title":"DEM-based extraction and analysis of digital river network of Tarim River","volume":"51","author":"Ning","year":"2020","journal-title":"Water Resour. 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