{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T19:03:21Z","timestamp":1793127801885,"version":"4.5.0"},"reference-count":47,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:00:00Z","timestamp":1750204800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2023YFC3007202"],"award-info":[{"award-number":["2023YFC3007202"]}]},{"name":"National Key R&amp;D Program of China","award":["NRMSSHR2022Z09"],"award-info":[{"award-number":["NRMSSHR2022Z09"]}]},{"name":"National Key R&amp;D Program of China","award":["AR2408"],"award-info":[{"award-number":["AR2408"]}]},{"name":"Open Fund of the Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region, Ministry of Natural Resources","award":["2023YFC3007202"],"award-info":[{"award-number":["2023YFC3007202"]}]},{"name":"Open Fund of the Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region, Ministry of Natural Resources","award":["NRMSSHR2022Z09"],"award-info":[{"award-number":["NRMSSHR2022Z09"]}]},{"name":"Open Fund of the Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region, Ministry of Natural Resources","award":["AR2408"],"award-info":[{"award-number":["AR2408"]}]},{"name":"Fundamental Research Funds from the Chinese Academy of Surveying and Mapping","award":["2023YFC3007202"],"award-info":[{"award-number":["2023YFC3007202"]}]},{"name":"Fundamental Research Funds from the Chinese Academy of Surveying and Mapping","award":["NRMSSHR2022Z09"],"award-info":[{"award-number":["NRMSSHR2022Z09"]}]},{"name":"Fundamental Research Funds from the Chinese Academy of Surveying and Mapping","award":["AR2408"],"award-info":[{"award-number":["AR2408"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To address existing issues in water extraction from SAR images based on deep learning, such as confusion between mountain shadows and water bodies and difficulty in extracting complex boundary details for continuous water bodies, the LGFUNet model is proposed. The LGFUNet model consists of three parts: the encoder\u2013decoder, the DECASPP module, and the LGFF module. In the encoder\u2013decoder, the Swin-Transformer module is used instead of convolution kernels for feature extraction, enhancing the learning of global information and improving the model\u2019s ability to capture the spatial features of continuous water bodies. The DECASPP module is employed to extract and select multiscale features, focusing on complex water body boundary details. Additionally, a series of LGFF modules are inserted between the encoder and decoder to reduce the semantic gap between the encoder and decoder feature maps and the spatial information loss caused by the encoder\u2019s downsampling process, improving the model\u2019s ability to learn detailed information. Sentinel-1 SAR data from the Qinghai\u2013Tibet Plateau region are selected, and the water extraction performance of the proposed LGFUNet model is compared with that of existing methods such as U-Net, Swin-UNet, and SCUNet++. The results show that the LGFUNet model achieves the best performance, respectively.<\/jats:p>","DOI":"10.3390\/s25123814","type":"journal-article","created":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T09:36:00Z","timestamp":1750239360000},"page":"3814","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["LGFUNet: A Water Extraction Network in SAR Images Based on Multiscale Local Features with Global Information"],"prefix":"10.3390","volume":"25","author":[{"given":"Xiaowei","family":"Bai","sequence":"first","affiliation":[{"name":"Chinese Academy of Surveying and Mapping, Beijing 100036, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yonghong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Chinese Academy of Surveying and Mapping, Beijing 100036, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jujie","family":"Wei","sequence":"additional","affiliation":[{"name":"Chinese Academy of Surveying and Mapping, Beijing 100036, China"},{"name":"Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region, Ministry of Natural Resources, Changsha 410118, Hunan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yang, M., Wu, G., Zhu, S., and Li, S. 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