A SAR-based deep multi-feature fusion network (MDFLD-Net) for water leakage detection

Traditional manual inspections for urban water supply network leak detection face challenges such as high costs, low efficiency, and limited coverage. Microwave remote sensing technology, offering a wide detection range and high operational efficiency, has emerged as a promising approach for large-scale leakage detection. However, in complex urban environments, existing microwave-based methods often struggle to effectively extract distinctive features of leakage-induced anomalies, thereby limiting detection accuracy. To overcome this limitation, we propose a Multi-feature Deep Fusion Leakage Detection Network (MDFLD-Net) that integrates backscatter intensity, polarimetric decomposition, and closure phase features—complementary observables that mitigate surface disturbances and enhance sensitivity to leakage-related soil moisture anomalies. The network employs multi-scale feature extraction modules (MSFE) to hierarchically capture both local details and global semantic patterns, abnormal frequency extraction modules (AFE) to capture the key frequency-domain characteristics of leakage, and deformable convolution modules (DC) in the fusion stage to refine spatial adaptability. Experimental results show that MDFLD-Net achieves a leakage identification accuracy of 86.3%, demonstrating its robustness and effectiveness in leakage detection.