Swin-GAT Fusion Dual-Stream Hybrid Network for High-Resolution Remote Sensing Road Extraction
This paper introduces a novel dual-stream collaborative architecture for remote sensing road segmentation, designed to overcome multi-scale feature conflicts, limited dynamic adaptability, and compromised topological integrity. Our network employs a parallel “local–global” encoding scheme: the local...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/13/2238 |
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| author | Hongkai Zhang Hongxuan Yuan Minghao Shao Junxin Wang Suhong Liu |
| author_facet | Hongkai Zhang Hongxuan Yuan Minghao Shao Junxin Wang Suhong Liu |
| author_sort | Hongkai Zhang |
| collection | DOAJ |
| description | This paper introduces a novel dual-stream collaborative architecture for remote sensing road segmentation, designed to overcome multi-scale feature conflicts, limited dynamic adaptability, and compromised topological integrity. Our network employs a parallel “local–global” encoding scheme: the local stream uses depth-wise separable convolutions to capture fine-grained details, while the global stream integrates a Swin-Transformer with a graph-attention module (Swin-GAT) to model long-range contextual and topological relationships. By decoupling detailed feature extraction from global context modeling, the proposed framework more faithfully represents complex road structures. Comprehensive experiments on multiple aerial datasets demonstrate that our approach outperforms conventional baselines—especially under shadow occlusion and for thin-road delineation—while achieving real-time inference at 31 FPS. Ablation studies further confirm the critical roles of the Swin Transformer and GAT components in preserving topological continuity. Overall, this dual-stream dynamic-fusion network sets a new benchmark for remote sensing road extraction and holds promise for real-world, real-time applications. |
| format | Article |
| id | doaj-art-8008a9c3b90a42d3aa098348dfbf05d1 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-8008a9c3b90a42d3aa098348dfbf05d12025-08-20T02:36:27ZengMDPI AGRemote Sensing2072-42922025-06-011713223810.3390/rs17132238Swin-GAT Fusion Dual-Stream Hybrid Network for High-Resolution Remote Sensing Road ExtractionHongkai Zhang0Hongxuan Yuan1Minghao Shao2Junxin Wang3Suhong Liu4Faculty of Arts and Sciences, Beijing Normal University, Zhuhai 519087, ChinaFaculty of Arts and Sciences, Beijing Normal University, Zhuhai 519087, ChinaFaculty of Arts and Sciences, Beijing Normal University, Zhuhai 519087, ChinaFaculty of Arts and Sciences, Beijing Normal University, Zhuhai 519087, ChinaDepartment of Geography, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai 519087, ChinaThis paper introduces a novel dual-stream collaborative architecture for remote sensing road segmentation, designed to overcome multi-scale feature conflicts, limited dynamic adaptability, and compromised topological integrity. Our network employs a parallel “local–global” encoding scheme: the local stream uses depth-wise separable convolutions to capture fine-grained details, while the global stream integrates a Swin-Transformer with a graph-attention module (Swin-GAT) to model long-range contextual and topological relationships. By decoupling detailed feature extraction from global context modeling, the proposed framework more faithfully represents complex road structures. Comprehensive experiments on multiple aerial datasets demonstrate that our approach outperforms conventional baselines—especially under shadow occlusion and for thin-road delineation—while achieving real-time inference at 31 FPS. Ablation studies further confirm the critical roles of the Swin Transformer and GAT components in preserving topological continuity. Overall, this dual-stream dynamic-fusion network sets a new benchmark for remote sensing road extraction and holds promise for real-world, real-time applications.https://www.mdpi.com/2072-4292/17/13/2238remote sensing road extractiondual-stream networkSwin-GATdepthwise separable convolutiondynamic feature fusion |
| spellingShingle | Hongkai Zhang Hongxuan Yuan Minghao Shao Junxin Wang Suhong Liu Swin-GAT Fusion Dual-Stream Hybrid Network for High-Resolution Remote Sensing Road Extraction Remote Sensing remote sensing road extraction dual-stream network Swin-GAT depthwise separable convolution dynamic feature fusion |
| title | Swin-GAT Fusion Dual-Stream Hybrid Network for High-Resolution Remote Sensing Road Extraction |
| title_full | Swin-GAT Fusion Dual-Stream Hybrid Network for High-Resolution Remote Sensing Road Extraction |
| title_fullStr | Swin-GAT Fusion Dual-Stream Hybrid Network for High-Resolution Remote Sensing Road Extraction |
| title_full_unstemmed | Swin-GAT Fusion Dual-Stream Hybrid Network for High-Resolution Remote Sensing Road Extraction |
| title_short | Swin-GAT Fusion Dual-Stream Hybrid Network for High-Resolution Remote Sensing Road Extraction |
| title_sort | swin gat fusion dual stream hybrid network for high resolution remote sensing road extraction |
| topic | remote sensing road extraction dual-stream network Swin-GAT depthwise separable convolution dynamic feature fusion |
| url | https://www.mdpi.com/2072-4292/17/13/2238 |
| work_keys_str_mv | AT hongkaizhang swingatfusiondualstreamhybridnetworkforhighresolutionremotesensingroadextraction AT hongxuanyuan swingatfusiondualstreamhybridnetworkforhighresolutionremotesensingroadextraction AT minghaoshao swingatfusiondualstreamhybridnetworkforhighresolutionremotesensingroadextraction AT junxinwang swingatfusiondualstreamhybridnetworkforhighresolutionremotesensingroadextraction AT suhongliu swingatfusiondualstreamhybridnetworkforhighresolutionremotesensingroadextraction |