Water-Body Detection from SAR Images Using Connectivity Refinement Network

Synthetic aperture radar (SAR) is an active microwave imaging system equipped with penetration capability, enabling all-time and all-weather Earth observation, and demonstrates significant advantages in large-scale surface water-body detection. Although SAR images can provide relatively clear water-body details, they are susceptible to interference from external factors such as complex terrain and background noise, resulting in fragmented detection outcomes and poor connectivity. Therefore, a Connectivity Refinement Network (ConRNet) is proposed in this study to address the issue of fragmented water-body regions in water-body detection results, combining HISEA-1 and Chaohu-1 SAR data. ConRNet is equipped with attention mechanisms and a connectivity prediction module, combined with dual supervision from segmentation and connectivity labels. Unlike conventional attention modules that only emphasize pixel-wise saliency, the proposed Dual Self-Attention Module (DSAM) jointly captures spatial and channel dependencies. Meanwhile, the Connectivity Prediction Module (CPM) reformulates water-body connectivity as a regression problem to directly optimize structural coherence without relying on post-processing. Leveraging dual supervision from segmentation and connectivity labels, ConRNet achieves simultaneous improvements in topological consistency and pixel-level accuracy. The performance of the proposed ConRNet is evaluated by con-ducting comparative experiments with five deep learning models: FCN, U-Net, DeepLabv3+, HRNet, and MAGNet. The experimental results demonstrate that the ConRNet achieves the highest accuracy in water-body detection, with an intersection over union (IoU) of 88.59% and an F1-score of 93.87%.