Detection of Building Damage due to the 2016 Kumamoto Earthquake using Texture Combinations of Single Post-event Airborne SAR Data

This paper investigates the 2016 Kumamoto Earthquake to explore the use of post-event full polarimetric SAR images in building damage assessment. In this study, one post-event airborne SAR image of Pi-SAR-X2 was employed to detect the changes of residential buildings in Mashiki Town, Kumamoto Prefecture, Japan. The changes in scattering characteristics of collapsed and non-collapsed buildings were compared using variance, homogeneity, contrast and dissimilarity textures of the Grey Level Co-occurrence Matrix (GLCM) from building footprints and roof areas. In addition, due to the difficulty in detection using only one texture, combination of textures in different polarizations were also applied for damage detection. The results of this method were then compared with the field survey data. The result indicates the possibility of building damage assessment using only post-event data and solving the pre-event data limitation problem.