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 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.