A PolSAR Bridge Detection Method Integrating Cross-Sectional Probability Modeling and Graph Topology Analysis

Accurate bridge detection in polarimetric SAR (PolSAR) imagery remains challenging due to the diversity of bridges, strong speckle noise, and complex backgrounds. Bridges spanning narrow river branches are particularly prone to missed detection. We propose a bridge detection method that combines cross-section probability modeling (CPM) and graph topology analysis (GTA) for accurate detection of bridges over narrow river branches in complex water network. The core innovation of the proposed bridge detection method lies in a novel approach to constructing water networks. Specifically, cross-sections are extracted at the termini of water branches, which are subsequently connected to construct a water network. Bridges are detected as land regions that connect adjacent branches of the water network. Water regions are first segmented from PolSAR images using a likelihood ratio test. Subsequently, cross-sections at the ends of water branches are estimated via a particle filtering algorithm, ensuring precise localization and shape representation of branch termini. Finally, matched cross-section pairs are used to construct the water network, enabling the reliable detection of bridge regions. Experiments on Gaofen-3 (GF-3) and RADARSAT-2 datasets covering single-branch, multi-branch, and complex waterway scenarios demonstrate the effectiveness of the proposed approach. Compared with existing methods, it achieves superior performance with an F1-score of 0.94 and a mIoU of 0.66, confirming its robustness and accuracy.