{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T18:35:02Z","timestamp":1780511702385,"version":"3.54.1"},"reference-count":46,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T00:00:00Z","timestamp":1765238400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Liaoning Applied Basic Research Program Fund","award":["2023JH2\/101300239"],"award-info":[{"award-number":["2023JH2\/101300239"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Monitoring surface water bodies is crucial for environmental protection and resource management. Existing segmentation methods often struggle with limited generalization across different satellite domains. We propose DASAM, a domain-adaptive Segment Anything Model for cross-domain water body segmentation in satellite imagery. The core innovation of DASAM is a contrastive learning module that aligns features between source and style-augmented images, enabling robust domain generalization without requiring annotations from the target domain. Additionally, DASAM integrates a prompt-enhanced module and an encoder adapter to capture fine-grained spatial details and global context, further improving segmentation accuracy. Experiments on the China GF-2 dataset demonstrate superior performance over existing methods, while cross-domain evaluations on GLH-water and Sentinel-2 water body image datasets verify its strong generalization and robustness. These results highlight DASAM\u2019s potential for large-scale, diverse satellite water body monitoring and accurate environmental analysis.<\/jats:p>","DOI":"10.3390\/jimaging11120437","type":"journal-article","created":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T12:42:43Z","timestamp":1765284163000},"page":"437","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Domain-Adaptive Segment Anything Model for Cross-Domain Water Body Segmentation in Satellite Imagery"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4364-9817","authenticated-orcid":false,"given":"Lihong","family":"Yang","sequence":"first","affiliation":[{"name":"Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110169, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengfei","family":"Liu","sequence":"additional","affiliation":[{"name":"Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110169, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guilong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110169, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7772-8652","authenticated-orcid":false,"given":"Huaici","family":"Zhao","sequence":"additional","affiliation":[{"name":"Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110169, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunyang","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1869","DOI":"10.1007\/s11042-023-15764-5","article-title":"Critical review on deep learning methodologies employed for water-body segmentation through remote sensing images","volume":"83","author":"Gautam","year":"2023","journal-title":"Multimed. 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