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 |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 2045-2322 |