A Land Bridge Extraction Method Based on Polarized Circular Synthetic Aperture Radar Images

As important man-made targets, bridges have been a major focus of Synthetic Aperture Radar (SAR) image interpretation, and many researchers have developed methods for bridge detection. The core frameworks of these methods are analogical, a river is first extracted, and a water bridge is detected based on the positional relationship between the river and bridge. However, existing bridge detection methods relying on river extraction; cannot be utilized detect land bridges. This is because the background environment under a bridge is land, not river, which has different scattering characteristics and shape layouts. As such, the traditional method for extracting rivers is not suitable for extracting land background, and it is impossible to locate a bridge based on prior knowledge of its location of. To resolve this problem, in this study, we propose a land bridge detection method based on polarized Circular SAR (CSAR) images. In our proposed method, the Circular Polarization Entropy (CPE) of an observed scene is introduced to separate possible bridge targets from a land background (In our experiment, the average CPE of the bridge is 0.4018, and that of the land background is 0.7819; thus there is a clear difference between the bridge and background). False targets are removed based on the difference in the polarization entropy variance features of the bridges and other ground objects; and the size characteristics of the bridges. Finally, accurate extractions of land bridges are obtained based on the geometric characteristics of the bridges. Experimental results based on real airborne L-band polarized CSAR data verify the correctness of the theoretical analysis and effectiveness of the proposed method.