{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T19:30:04Z","timestamp":1782243004540,"version":"3.54.5"},"reference-count":37,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T00:00:00Z","timestamp":1601510400000},"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":["41201468, 41701536, 61701047 and 41941019"],"award-info":[{"award-number":["41201468, 41701536, 61701047 and 41941019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Funds for the Central Universities, CHD","award":["300102260301\/087,300102260404\/087"],"award-info":[{"award-number":["300102260301\/087,300102260404\/087"]}]},{"name":"Hunan Provincial Education Department","award":["16B004,18A148"],"award-info":[{"award-number":["16B004,18A148"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Water detection from Synthetic Aperture Radar (SAR) images has been widely utilized in various applications. However, it remains an open challenge due to the high similarity between water and shadow in SAR images. To address this challenge, a new end-to-end framework based on deep learning has been proposed to automatically classify water and shadow areas in SAR images. This end-to-end framework is mainly composed of three parts, namely, Multi-scale Spatial Feature (MSF) extraction, Multi-Level Selective Attention Network (MLSAN) and the Improvement Strategy (IS). Firstly, the dataset is input to MSF for multi-scale low-level feature extraction via three different methods. Then, these low-level features are fed into the MLSAN network, which contains the Encoder and Decoder. The Encoder aims to generate different levels of features using residual network of 101 layers. The Decoder extracts geospatial contextual information and fuses the multi-level features to generate high-level features that are further optimized by the IS. Finally, the classification is implemented with the Softmax function. We name the proposed framework as MSF-MLSAN, which is trained and tested using millimeter wave SAR datasets. The classification accuracy reaches 0.8382 and 0.9278 for water and shadow, respectively; while the overall Intersection over Union (IoU) is 0.9076. MSF-MLSAN demonstrates the success of integrating SAR domain knowledge and state-of-the-art deep learning techniques.<\/jats:p>","DOI":"10.3390\/rs12193205","type":"journal-article","created":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T09:04:12Z","timestamp":1601543052000},"page":"3205","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["A Multi-Scale Deep Neural Network for Water Detection from SAR Images in the Mountainous Areas"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2432-9583","authenticated-orcid":false,"given":"Lifu","family":"Chen","sequence":"first","affiliation":[{"name":"School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114, China"},{"name":"Laboratory of Radar Remote Sensing Applications, Changsha University of Science and Technology, Changsha 410114, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114, China"},{"name":"Laboratory of Radar Remote Sensing Applications, Changsha University of Science and Technology, Changsha 410114, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5693-3414","authenticated-orcid":false,"given":"Jin","family":"Xing","sequence":"additional","affiliation":[{"name":"School of Engineering, Newcastle University, Newcastle NE17RU, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8054-7449","authenticated-orcid":false,"given":"Zhenhong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Engineering, Newcastle University, Newcastle NE17RU, UK"},{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuemin","family":"Xing","sequence":"additional","affiliation":[{"name":"Laboratory of Radar Remote Sensing Applications, Changsha University of Science and Technology, Changsha 410114, China"},{"name":"School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha 410114, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7100-826X","authenticated-orcid":false,"given":"Zhihui","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114, China"},{"name":"Laboratory of Radar Remote Sensing Applications, Changsha University of Science and Technology, Changsha 410114, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,1]]},"reference":[{"key":"ref_1","first-page":"156","article-title":"InSAR Principles-Guidelines for SAR Interferometry Processing and Interpretation","volume":"19","author":"Ferretti","year":"2007","journal-title":"J. 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