DMCF-Net: Dilated Multiscale Context Fusion Network for SAR Flood Detection
Synthetic aperture radar (SAR) imagery, with its all-weather, all-time capabilities, plays a critical role in flood detection. However, due to the diverse scattering mechanisms of water bodies, flood regions in SAR images typically exhibit high intraclass variance and low interclass variance. Additi...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11059328/ |
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| author | Zhimin Wang Lingli Zhao Nan Jiang Weidong Sun Jie Yang Lei Shi Hongtao Shi Pingxiang Li |
| author_facet | Zhimin Wang Lingli Zhao Nan Jiang Weidong Sun Jie Yang Lei Shi Hongtao Shi Pingxiang Li |
| author_sort | Zhimin Wang |
| collection | DOAJ |
| description | Synthetic aperture radar (SAR) imagery, with its all-weather, all-time capabilities, plays a critical role in flood detection. However, due to the diverse scattering mechanisms of water bodies, flood regions in SAR images typically exhibit high intraclass variance and low interclass variance. Additionally, the complex shapes and blurred boundaries of flood regions make it challenging for single-scale convolution methods to accurately identify them. To address this issue, we propose a novel deep learning approach, DMCF-Net, to effectively capture the intricate characteristics of flood regions in SAR imagery. DMCF-Net consists of three main modules: multiscale feature aggregation (MSFA) module, cross-scale attention fusion (CSAF) module, and deep feature refinement (DFR) module. MSFA module extracts multiscale features using a dual-branch approach with dilated and depthwise separable convolutions. CSAF module combines contextual information from neighboring scales, using edge details from shallow features and semantic information from deep features. DFR module uses convolutions with varying kernel sizes to refine the deepest features, improving the accuracy of flood detection. The effectiveness of DMCF-Net is assessed on the Sen1Floods11 dataset. Experimental results show that DMCF-Net outperforms other deep learning models, achieving an F1 score of 81.6% and an intersection over union of 68.9%, while also having lower computational cost (97.4G) and fewer parameters (16.4M). |
| format | Article |
| id | doaj-art-882b46677a664c4f9f82fd7720b2a2fe |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-882b46677a664c4f9f82fd7720b2a2fe2025-08-20T03:51:04ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118165491656110.1109/JSTARS.2025.358428211059328DMCF-Net: Dilated Multiscale Context Fusion Network for SAR Flood DetectionZhimin Wang0Lingli Zhao1https://orcid.org/0000-0001-8838-224XNan Jiang2Weidong Sun3https://orcid.org/0000-0001-8718-1710Jie Yang4Lei Shi5https://orcid.org/0000-0001-7567-5510Hongtao Shi6https://orcid.org/0000-0003-4054-4240Pingxiang Li7School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, ChinaSchool of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, ChinaSynthetic aperture radar (SAR) imagery, with its all-weather, all-time capabilities, plays a critical role in flood detection. However, due to the diverse scattering mechanisms of water bodies, flood regions in SAR images typically exhibit high intraclass variance and low interclass variance. Additionally, the complex shapes and blurred boundaries of flood regions make it challenging for single-scale convolution methods to accurately identify them. To address this issue, we propose a novel deep learning approach, DMCF-Net, to effectively capture the intricate characteristics of flood regions in SAR imagery. DMCF-Net consists of three main modules: multiscale feature aggregation (MSFA) module, cross-scale attention fusion (CSAF) module, and deep feature refinement (DFR) module. MSFA module extracts multiscale features using a dual-branch approach with dilated and depthwise separable convolutions. CSAF module combines contextual information from neighboring scales, using edge details from shallow features and semantic information from deep features. DFR module uses convolutions with varying kernel sizes to refine the deepest features, improving the accuracy of flood detection. The effectiveness of DMCF-Net is assessed on the Sen1Floods11 dataset. Experimental results show that DMCF-Net outperforms other deep learning models, achieving an F1 score of 81.6% and an intersection over union of 68.9%, while also having lower computational cost (97.4G) and fewer parameters (16.4M).https://ieeexplore.ieee.org/document/11059328/Deep learningflood detectionmultiscale featuressen1floods11synthetic aperture radar (SAR) |
| spellingShingle | Zhimin Wang Lingli Zhao Nan Jiang Weidong Sun Jie Yang Lei Shi Hongtao Shi Pingxiang Li DMCF-Net: Dilated Multiscale Context Fusion Network for SAR Flood Detection IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Deep learning flood detection multiscale features sen1floods11 synthetic aperture radar (SAR) |
| title | DMCF-Net: Dilated Multiscale Context Fusion Network for SAR Flood Detection |
| title_full | DMCF-Net: Dilated Multiscale Context Fusion Network for SAR Flood Detection |
| title_fullStr | DMCF-Net: Dilated Multiscale Context Fusion Network for SAR Flood Detection |
| title_full_unstemmed | DMCF-Net: Dilated Multiscale Context Fusion Network for SAR Flood Detection |
| title_short | DMCF-Net: Dilated Multiscale Context Fusion Network for SAR Flood Detection |
| title_sort | dmcf net dilated multiscale context fusion network for sar flood detection |
| topic | Deep learning flood detection multiscale features sen1floods11 synthetic aperture radar (SAR) |
| url | https://ieeexplore.ieee.org/document/11059328/ |
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