{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:34:43Z","timestamp":1783438483354,"version":"3.54.6"},"reference-count":48,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,15]],"date-time":"2024-09-15T00:00:00Z","timestamp":1726358400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"General Program of the China National Natural Science Foundation","award":["42171124"],"award-info":[{"award-number":["42171124"]}]},{"name":"General Program of the China National Natural Science Foundation","award":["42330512"],"award-info":[{"award-number":["42330512"]}]},{"name":"Key Program of China National Natural Science Foundation","award":["42171124"],"award-info":[{"award-number":["42171124"]}]},{"name":"Key Program of China National Natural Science Foundation","award":["42330512"],"award-info":[{"award-number":["42330512"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Snow cover distribution is of great significance for climate change and water resource management. Current deep learning-based methods for extracting snow cover from remote sensing images face challenges such as insufficient local detail awareness and inadequate utilization of global semantic information. In this study, a snow cover extraction algorithm integrating cross-scale edge perception and an attention mechanism on the U-net model architecture is proposed. The cross-scale edge perception module replaces the original jump connection of U-net, enhances the low-level image features by introducing edge detection on the shallow feature scale, and enhances the detail perception via branch separation and fusion features on the deep feature scale. Meanwhile, parallel channel and spatial attention mechanisms are introduced in the model encoding stage to adaptively enhance the model\u2019s attention to key features and improve the efficiency of utilizing global semantic information. The method was evaluated on the publicly available CSWV_S6 optical remote sensing dataset, and the accuracy of 98.14% indicates that the method has significant advantages over existing methods. Snow extraction from Landsat 8 OLI images of the upper reaches of the Irtysh River was achieved with satisfactory accuracy rates of 95.57% (using two, three, and four bands) and 96.65% (using two, three, four, and six bands), indicating its strong potential for automated snow cover extraction over larger areas.<\/jats:p>","DOI":"10.3390\/rs16183430","type":"journal-article","created":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T10:56:57Z","timestamp":1726484217000},"page":"3430","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Snow Cover Extraction from Landsat 8 OLI Based on Deep Learning with Cross-Scale Edge-Aware and Attention Mechanism"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0821-9690","authenticated-orcid":false,"given":"Zehao","family":"Yu","sequence":"first","affiliation":[{"name":"College of Urban and Environmental Science, Northwest University, Xi\u2019an 710127, China"},{"name":"Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Northwest University, Xi\u2019an 710127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanying","family":"Gong","sequence":"additional","affiliation":[{"name":"College of Urban and Environmental Science, Northwest University, Xi\u2019an 710127, China"},{"name":"Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Northwest University, Xi\u2019an 710127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3407-6356","authenticated-orcid":false,"given":"Shiqiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Urban and Environmental Science, Northwest University, Xi\u2019an 710127, China"},{"name":"Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Northwest University, Xi\u2019an 710127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Urban and Environmental Science, Northwest University, Xi\u2019an 710127, China"},{"name":"Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Northwest University, Xi\u2019an 710127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2017.04.023","article-title":"Global multisensor automated satellite-based snow and ice mapping system (GMASI) for cryosphere monitoring","volume":"196","author":"Romanov","year":"2017","journal-title":"Remote Sens. 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