Imaging Simulation and Damage Assessment Feature Analysis of Ku Band Polarized SAR of Buildings

Abstract

Building damage assessment is important in disaster emergency monitoring. In recent years, with the increase of multi-polarization capability of Synthetic Aperture Radar (SAR), Polarimetric Synthetic Aperture Radar (PolSAR) provides more possibilities for building damage assessment, and the polarization-characteristic-based building damage assessment method has gradually become the focus of research. However, because of the limitations of data acquisition in PolSAR, current research mainly focuses on the L, C, X, and other limited bands. To obtain an in depth understanding of the polarization characteristics of damaged buildings in SAR images and develop the application of the polarization characteristics of damaged buildings to other bands, this study conducted a simulation experiment of Ku band polarized SAR of buildings, and performed damage assessment feature analysis using the SAR image polarization decomposition method. In this study, a scale model of real materials was built and the “microwave characteristic measurement and simulation imaging scientific experiment platform” was used to conduct SAR simulation imaging of the target buildings. The Ku band polarized SAR images before and after building damage were obtained. Then, the polarization scattering characteristics of buildings before and after damage were analyzed using various common polarization decomposition methods such as \begin{document}$ {H/A/\alpha} $\end{document} decomposition, Yamaguchi decomposition and Touzi decomposition. Results show that the disoriented volume scattering component and the proportion of the disoriented secondary scattering component obtained by the Yamaguchi decomposition and the \begin{document}${ {\alpha }_{\rm s1}} $\end{document} component obtained by the Touzi decomposition have good indicative significance for building damage assessment in the Ku band. Compared with the X band measurement results, the Ku band is more sensitive to building damage assessment, which has important implications for future radar remote sensing applications.