{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T11:45:57Z","timestamp":1774352757444,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T00:00:00Z","timestamp":1731283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific and Technological Innovation 2030\u2014Major Projects","award":["NO.2023ZD0405605"],"award-info":[{"award-number":["NO.2023ZD0405605"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Semantic segmentation of vegetation in aerial remote sensing images is a critical aspect of vegetation mapping. Accurate vegetation segmentation effectively informs real-world production and construction activities. However, the presence of species heterogeneity, seasonal variations, and feature disparities within remote sensing images poses significant challenges for vision tasks. Traditional machine learning-based methods often struggle to capture deep-level features for the segmentation. This work proposes a novel deep learning network named FA-HRNet that leverages the fusion of attention mechanism and a multi-branch network structure for vegetation detection and segmentation. Quantitative analysis from multiple datasets reveals that our method outperforms existing approaches, with improvements in MIoU and PA by 2.17% and 4.85%, respectively, compared with the baseline network. Our approach exhibits significant advantages over the other methods regarding cross-region and cross-scale capabilities, providing a reliable vegetation coverage ratio for ecological analysis.<\/jats:p>","DOI":"10.3390\/rs16224194","type":"journal-article","created":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T11:34:11Z","timestamp":1731324851000},"page":"4194","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["FA-HRNet: A New Fusion Attention Approach for Vegetation Semantic Segmentation and Analysis"],"prefix":"10.3390","volume":"16","author":[{"given":"Bingnan","family":"He","sequence":"first","affiliation":[{"name":"College of Information Science and Technology and Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, China"}]},{"given":"Dongyang","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology and Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, China"}]},{"given":"Li","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology and Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, China"}]},{"given":"Sheng","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology and Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.1007\/s10708-019-10037-x","article-title":"Using Landsat satellite data for assessing the land use and land cover change in Kashmir valley","volume":"85","author":"Alam","year":"2020","journal-title":"GeoJournal"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Gao, Y., Shao, Y., Jiang, R., Yang, X., and Zhang, L. 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