{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T16:57:53Z","timestamp":1769014673879,"version":"3.49.0"},"reference-count":58,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T00:00:00Z","timestamp":1667520000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Fundamental Research Funds for the Central Universities","award":["XJS221307"],"award-info":[{"award-number":["XJS221307"]}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["JB211312"],"award-info":[{"award-number":["JB211312"]}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["XJS221307"],"award-info":[{"award-number":["XJS221307"]}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["JB211312"],"award-info":[{"award-number":["JB211312"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Water body segmentation is an important tool for the hydrological monitoring of the Earth. With the rapid development of convolutional neural networks, semantic segmentation techniques have been used on remote sensing images to extract water bodies. However, some difficulties need to be overcome to achieve good results in water body segmentation, such as complex background, huge scale, water connectivity, and rough edges. In this study, a water body segmentation model (DUPnet) with dense connectivity and multi-scale pyramidal pools is proposed to rapidly and accurately extract water bodies from Gaofen satellite and Landsat 8 OLI (Operational Land Imager) images. The proposed method includes three parts: (1) a multi-scale spatial pyramid pooling module (MSPP) is introduced to combine shallow and deep features for small water bodies and to compensate for the feature loss caused by the sampling process; (2) dense blocks are used to extract more spatial features to DUPnet\u2019s backbone, increasing feature propagation and reuse; (3) a regression loss function is proposed to train the network to deal with the unbalanced dataset caused by small water bodies. The experimental results show that the F1, MIoU, and FWIoU of DUPnet on the 2020 Gaofen dataset are 97.67%, 88.17%, and 93.52%, respectively, and on the Landsat River dataset, they are 96.52%, 84.72%, 91.77%, respectively.<\/jats:p>","DOI":"10.3390\/rs14215567","type":"journal-article","created":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T09:23:37Z","timestamp":1667553817000},"page":"5567","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["DUPnet: Water Body Segmentation with Dense Block and Multi-Scale Spatial Pyramid Pooling for Remote Sensing Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2794-3557","authenticated-orcid":false,"given":"Zhiheng","family":"Liu","sequence":"first","affiliation":[{"name":"School of Aerospace Science and Technology, Xidian University, Xi\u2019an 710026, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9212-2141","authenticated-orcid":false,"given":"Xuemei","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Aerospace Science and Technology, Xidian University, Xi\u2019an 710026, China"}]},{"given":"Suiping","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Aerospace Science and Technology, Xidian University, Xi\u2019an 710026, China"}]},{"given":"Hang","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Aerospace Science and Technology, Xidian University, Xi\u2019an 710026, China"}]},{"given":"Jianhua","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Aerospace and Geodesy, Data Science in Earth Observation, Technical University of Munich (TUM), 80333 Munich, Germany"}]},{"given":"Yanming","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Aerospace Science and Technology, Xidian University, Xi\u2019an 710026, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.isprsjprs.2022.03.013","article-title":"Water body classification from high-resolution optical remote sensing imagery: Achievements and perspectives","volume":"187","author":"Li","year":"2022","journal-title":"ISPRS J. 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