A SAR-based flood mapping approach: application of SAR-SIFT registration and modified DeepLabV3 segmentation in flood hazard assessment
An innovative framework for rapid flood detection leverages Synthetic Aperture Radar (SAR) to overcome cloud obstruction and geolocation inaccuracies. SAR-SIFT registration corrects spatial errors in domestic SAR imagery, while a lightweight Modified DeepLabV3 model—trained on multi-polarization SAR...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Geocarto International |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/10106049.2025.2512188 |
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| Summary: | An innovative framework for rapid flood detection leverages Synthetic Aperture Radar (SAR) to overcome cloud obstruction and geolocation inaccuracies. SAR-SIFT registration corrects spatial errors in domestic SAR imagery, while a lightweight Modified DeepLabV3 model—trained on multi-polarization SAR data (HH/HV) fused with terrain features (elevation, slope, aspect, gradient) and watershed—enhances floodwater extraction accuracy. A DEM gradient strategy mitigates terrain shadow interference, refining detection reliability. Experimental results achieve 0.9840 overall accuracy, 0.9631 precision, 0.882 recall, 0.9207 F1-score, and 0.8483 IoU. Despite Sentinel-1A’s 12-day revisit cycle limitations, integrated domestic SAR data ensures timely flood monitoring. The framework’s scalability and cost-efficiency enable large-scale deployment, outperforming optical sensors in cloud-prone scenarios. By combining advanced georegistration, terrain-adaptive algorithms, and multi-source data fusion, this solution strengthens disaster response capabilities, offering robust real-time flood mapping and early warning systems for improved emergency management. |
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| ISSN: | 1010-6049 1752-0762 |