A semi-supervised boundary segmentation network for remote sensing images
Abstract Accurately segmenting remote sensing images remains challenging due to the diverse target scales and ambiguous structural boundaries. In this work, we propose a semi-supervised boundary segmentation network (BS-GAN) to address these challenges. BS-GAN employs a semi-supervised learning appr...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-85125-9 |
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| _version_ | 1832594845920657408 |
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| author | Yongdong Chen Zaichun Yang Liangji Zhang Weiwei Cai |
| author_facet | Yongdong Chen Zaichun Yang Liangji Zhang Weiwei Cai |
| author_sort | Yongdong Chen |
| collection | DOAJ |
| description | Abstract Accurately segmenting remote sensing images remains challenging due to the diverse target scales and ambiguous structural boundaries. In this work, we propose a semi-supervised boundary segmentation network (BS-GAN) to address these challenges. BS-GAN employs a semi-supervised learning approach to reduce dependency on labeled data while introducing a novel mixed attention (MA) mechanism to enhance segmentation accuracy by aggregating long-range contextual information. Additionally, we develop a Boundary Gating Module (BGM) to refine boundary segmentation through a multi-task learning strategy focused on boundary feature enhancement. Experimental results on three benchmark datasets demonstrate that BS-GAN achieves superior accuracy and generalization capabilities compared to existing segmentation networks. |
| format | Article |
| id | doaj-art-9431e442c75547788cda40f2c90809f8 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-9431e442c75547788cda40f2c90809f82025-01-19T12:19:29ZengNature PortfolioScientific Reports2045-23222025-01-0115111610.1038/s41598-025-85125-9A semi-supervised boundary segmentation network for remote sensing imagesYongdong Chen0Zaichun Yang1Liangji Zhang2Weiwei Cai3Shaoxing University Yuanpei CollegeCollege of Computer and Mathematics, Central South University of Forestry and TechnologyCollege of Computer and Mathematics, Central South University of Forestry and TechnologyCollege of Computer and Mathematics, Central South University of Forestry and TechnologyAbstract Accurately segmenting remote sensing images remains challenging due to the diverse target scales and ambiguous structural boundaries. In this work, we propose a semi-supervised boundary segmentation network (BS-GAN) to address these challenges. BS-GAN employs a semi-supervised learning approach to reduce dependency on labeled data while introducing a novel mixed attention (MA) mechanism to enhance segmentation accuracy by aggregating long-range contextual information. Additionally, we develop a Boundary Gating Module (BGM) to refine boundary segmentation through a multi-task learning strategy focused on boundary feature enhancement. Experimental results on three benchmark datasets demonstrate that BS-GAN achieves superior accuracy and generalization capabilities compared to existing segmentation networks.https://doi.org/10.1038/s41598-025-85125-9 |
| spellingShingle | Yongdong Chen Zaichun Yang Liangji Zhang Weiwei Cai A semi-supervised boundary segmentation network for remote sensing images Scientific Reports |
| title | A semi-supervised boundary segmentation network for remote sensing images |
| title_full | A semi-supervised boundary segmentation network for remote sensing images |
| title_fullStr | A semi-supervised boundary segmentation network for remote sensing images |
| title_full_unstemmed | A semi-supervised boundary segmentation network for remote sensing images |
| title_short | A semi-supervised boundary segmentation network for remote sensing images |
| title_sort | semi supervised boundary segmentation network for remote sensing images |
| url | https://doi.org/10.1038/s41598-025-85125-9 |
| work_keys_str_mv | AT yongdongchen asemisupervisedboundarysegmentationnetworkforremotesensingimages AT zaichunyang asemisupervisedboundarysegmentationnetworkforremotesensingimages AT liangjizhang asemisupervisedboundarysegmentationnetworkforremotesensingimages AT weiweicai asemisupervisedboundarysegmentationnetworkforremotesensingimages AT yongdongchen semisupervisedboundarysegmentationnetworkforremotesensingimages AT zaichunyang semisupervisedboundarysegmentationnetworkforremotesensingimages AT liangjizhang semisupervisedboundarysegmentationnetworkforremotesensingimages AT weiweicai semisupervisedboundarysegmentationnetworkforremotesensingimages |