{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:25:04Z","timestamp":1760145904333,"version":"build-2065373602"},"reference-count":67,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T00:00:00Z","timestamp":1725753600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"advanced research on civil space technology during the 14th Five-Year Plan","award":["D040405","92037000","42375040","42161054","42205153","2022KFKTC003","NMICJY202307","NMICJY202305","202302035765"],"award-info":[{"award-number":["D040405","92037000","42375040","42161054","42205153","2022KFKTC003","NMICJY202307","NMICJY202305","202302035765"]}]},{"name":"National Science Foundation of China","award":["D040405","92037000","42375040","42161054","42205153","2022KFKTC003","NMICJY202307","NMICJY202305","202302035765"],"award-info":[{"award-number":["D040405","92037000","42375040","42161054","42205153","2022KFKTC003","NMICJY202307","NMICJY202305","202302035765"]}]},{"name":"Open Foundation of the Key Laboratory of Coupling Process and Effect of Natural Resources Elements","award":["D040405","92037000","42375040","42161054","42205153","2022KFKTC003","NMICJY202307","NMICJY202305","202302035765"],"award-info":[{"award-number":["D040405","92037000","42375040","42161054","42205153","2022KFKTC003","NMICJY202307","NMICJY202305","202302035765"]}]},{"name":"National Meteorological Information Center of China Meteorological","award":["D040405","92037000","42375040","42161054","42205153","2022KFKTC003","NMICJY202307","NMICJY202305","202302035765"],"award-info":[{"award-number":["D040405","92037000","42375040","42161054","42205153","2022KFKTC003","NMICJY202307","NMICJY202305","202302035765"]}]},{"name":"GHFUND C","award":["D040405","92037000","42375040","42161054","42205153","2022KFKTC003","NMICJY202307","NMICJY202305","202302035765"],"award-info":[{"award-number":["D040405","92037000","42375040","42161054","42205153","2022KFKTC003","NMICJY202307","NMICJY202305","202302035765"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Snow detection is imperative in remote sensing for various applications, including climate change monitoring, water resources management, and disaster warning. Recognizing the limitations of current deep learning algorithms in cloud and snow boundary segmentation, as well as issues like detail snow information loss and mountainous snow omission, this paper presents a novel snow detection network based on Swin-Transformer and U-shaped dual-branch encoder structure with geographic information (SD-GeoSTUNet), aiming to address the above issues. Initially, the SD-GeoSTUNet incorporates the CNN branch and Swin-Transformer branch to extract features in parallel and the Feature Aggregation Module (FAM) is designed to facilitate the detail feature aggregation via two branches. Simultaneously, an Edge-enhanced Convolution (EeConv) is introduced to promote snow boundary contour extraction in the CNN branch. In particular, auxiliary geographic information, including altitude, longitude, latitude, slope, and aspect, is encoded in the Swin-Transformer branch to enhance snow detection in mountainous regions. Experiments conducted on Levir_CS, a large-scale cloud and snow dataset originating from Gaofen-1, demonstrate that SD-GeoSTUNet achieves optimal performance with the values of 78.08%, 85.07%, and 92.89% for IoU_s, F1_s, and MPA, respectively, leading to superior cloud and snow boundary segmentation and thin cloud and snow detection. Further, ablation experiments reveal that integrating slope and aspect information effectively alleviates the omission of snow detection in mountainous areas and significantly exhibits the best vision under complex terrain. The proposed model can be used for remote sensing data with geographic information to achieve more accurate snow extraction, which is conducive to promoting the research of hydrology and agriculture with different geospatial characteristics.<\/jats:p>","DOI":"10.3390\/rs16173327","type":"journal-article","created":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T04:15:01Z","timestamp":1725855301000},"page":"3327","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Snow Detection in Gaofen-1 Multi-Spectral Images Based on Swin-Transformer and U-Shaped Dual-Branch Encoder Structure Network with Geographic Information"],"prefix":"10.3390","volume":"16","author":[{"given":"Yue","family":"Wu","sequence":"first","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Chunxiang","family":"Shi","sequence":"additional","affiliation":[{"name":"National Meteorological Information Center, Beijing 100044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2496-0295","authenticated-orcid":false,"given":"Runping","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Xiang","family":"Gu","sequence":"additional","affiliation":[{"name":"National Meteorological Information Center, Beijing 100044, China"}]},{"given":"Ruian","family":"Tie","sequence":"additional","affiliation":[{"name":"National Meteorological Information Center, Beijing 100044, China"}]},{"given":"Lingling","family":"Ge","sequence":"additional","affiliation":[{"name":"National Meteorological Information Center, Beijing 100044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5785-9989","authenticated-orcid":false,"given":"Shuai","family":"Sun","sequence":"additional","affiliation":[{"name":"National Meteorological Information Center, Beijing 100044, China"},{"name":"Key Laboratory of Coupling Process and Effect of Natural Resources Elements, Beijing 100055, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2004RG000157","article-title":"Influence of the seasonal snow cover on the ground thermal regime: An overview","volume":"43","author":"Zhang","year":"2005","journal-title":"Rev. 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