A Novel Method for Monitoring River Level Changes Under Bridges With Time Series SAR Images
River level monitoring is crucial for hydrological studies, providing essential information for flood forecasting, water resource management, and environmental protection. In this article, we present a novel method for monitoring river level under bridges using time-series Synthetic Aperture Radar i...
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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/11036625/ |
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| _version_ | 1849432293346639872 |
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| author | Yifan Wang Mofan Li Gen Li Zihan Hu Zehua Dong Han Li |
| author_facet | Yifan Wang Mofan Li Gen Li Zihan Hu Zehua Dong Han Li |
| author_sort | Yifan Wang |
| collection | DOAJ |
| description | River level monitoring is crucial for hydrological studies, providing essential information for flood forecasting, water resource management, and environmental protection. In this article, we present a novel method for monitoring river level under bridges using time-series Synthetic Aperture Radar images. First, we transfer a DeepLab V3+ network model for road segmentation to bridge segmentation, fine-tuning it with bridge scattering signal data, while a new loss supervision function CentroidLoss, has been added to the model to improve the integrity of the bridge signal segmentation. Furthermore, the Energy Accumulation Algorithm (EAA) is proposed to improve the accuracy of river level measurements in areas of low signal-to-noise ratio with noise such as ships and waves. Leveraging deep learning and EAA, the proposed approach accurately extracts bridge scattering signals and precisely estimates the peak positions of the bridge’s multiple scattering signals, enabling precise river level monitoring. Sentinel-1A and COSMO-SkyMed data were applied as inputs to our method, and the comparison between the river levels measured by the proposed method and those of local hydrological stations reveals submeter level estimation accuracy. |
| format | Article |
| id | doaj-art-6f2b77ae0aa24dc1b84616c2d422c4be |
| 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-6f2b77ae0aa24dc1b84616c2d422c4be2025-08-20T03:27:24ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118163721638410.1109/JSTARS.2025.357977511036625A Novel Method for Monitoring River Level Changes Under Bridges With Time Series SAR ImagesYifan Wang0https://orcid.org/0009-0009-2263-4622Mofan Li1https://orcid.org/0000-0002-7555-6930Gen Li2https://orcid.org/0000-0002-6922-9263Zihan Hu3https://orcid.org/0009-0008-4497-2668Zehua Dong4https://orcid.org/0000-0002-2327-3824Han Li5https://orcid.org/0000-0002-4469-2606Radar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing, ChinaInstitute of Remote Sensing Satellite, CAST, Beijing, ChinaRadar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing, ChinaRadar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing, ChinaRadar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing, ChinaRadar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing, ChinaRiver level monitoring is crucial for hydrological studies, providing essential information for flood forecasting, water resource management, and environmental protection. In this article, we present a novel method for monitoring river level under bridges using time-series Synthetic Aperture Radar images. First, we transfer a DeepLab V3+ network model for road segmentation to bridge segmentation, fine-tuning it with bridge scattering signal data, while a new loss supervision function CentroidLoss, has been added to the model to improve the integrity of the bridge signal segmentation. Furthermore, the Energy Accumulation Algorithm (EAA) is proposed to improve the accuracy of river level measurements in areas of low signal-to-noise ratio with noise such as ships and waves. Leveraging deep learning and EAA, the proposed approach accurately extracts bridge scattering signals and precisely estimates the peak positions of the bridge’s multiple scattering signals, enabling precise river level monitoring. Sentinel-1A and COSMO-SkyMed data were applied as inputs to our method, and the comparison between the river levels measured by the proposed method and those of local hydrological stations reveals submeter level estimation accuracy.https://ieeexplore.ieee.org/document/11036625/Bridgemultiple scatteringriver level monitoringSynthetic Aperture Radar (SAR)transfer learning |
| spellingShingle | Yifan Wang Mofan Li Gen Li Zihan Hu Zehua Dong Han Li A Novel Method for Monitoring River Level Changes Under Bridges With Time Series SAR Images IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Bridge multiple scattering river level monitoring Synthetic Aperture Radar (SAR) transfer learning |
| title | A Novel Method for Monitoring River Level Changes Under Bridges With Time Series SAR Images |
| title_full | A Novel Method for Monitoring River Level Changes Under Bridges With Time Series SAR Images |
| title_fullStr | A Novel Method for Monitoring River Level Changes Under Bridges With Time Series SAR Images |
| title_full_unstemmed | A Novel Method for Monitoring River Level Changes Under Bridges With Time Series SAR Images |
| title_short | A Novel Method for Monitoring River Level Changes Under Bridges With Time Series SAR Images |
| title_sort | novel method for monitoring river level changes under bridges with time series sar images |
| topic | Bridge multiple scattering river level monitoring Synthetic Aperture Radar (SAR) transfer learning |
| url | https://ieeexplore.ieee.org/document/11036625/ |
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