Four-component decomposition of Pi-SAR2 airborne measurements to assess urban damage of the 2016 Kumamoto earthquake

The collection of information following large-scale natural disasters is of paramount importance for emergency response and recovery efforts. Synthetic Aperture Radar (SAR) sensors can be used around the clock, making them suitable for emergency data acquisition. In recent years, airborne SAR systems have also been developed and utilized during experimental phases. Pi-SAR2 is an airborne SAR system installed on aircraft, capable of detecting objects as small as 30 centimeters from altitudes exceeding 10,000 meters, even in adverse conditions such as nighttime, inclement weather, or volcanic eruptions. This enables rapid situational awareness in emergency situations and holds promise for providing robust support in formulating response strategies. During the 2016 Kumamoto earthquake in Japan, which caused extensive damage including building collapses and landslides due to strong tremors, the National Institute of Information and Communications Technology (NICT) conducted observations of the affected areas using the airborne X-band synthetic aperture radar sensor Pi-SAR2 shortly after the earthquake. In this study, we utilized the fully polarimetric (HH, HV, VV, and VH) Pi-SAR2 data acquired during the 2016 Kumamoto earthquake to investigate the capabilities of X-band airborne SAR data in damage assessment in urban areas. Employing the four-component covariance matrix decomposition method, we decomposed the original fully polarimetric Pi-SAR2 data into four scattering powers, namely double-bounce scattering (Pd), volume scattering (Pv), surface scattering (Ps), and helix scattering (Pc). We explored the post-disaster backscattering characteristics of urban areas affected by the catastrophe.