Backscattering Characteristics of SAR Images in Damaged Buildings Due to the 2016 Kumamoto Earthquake

Most research on the extraction of earthquake-caused building damage using synthetic aperture radar (SAR) images used building damage certification assessments and the EMS-98-based evaluation as ground truth. However, these methods do not accurately assess the damage characteristics. The buildings identified as Major damage in the Japanese damage certification survey contain damage with various characteristics. If Major damage is treated as a single class, the parameters of SAR images will vary greatly, and the relationship between building damage and SAR images would not be properly evaluated. Therefore, it is necessary to divide Major damage buildings into more detailed classes. In this study, the Major damage buildings were newly classified into five damage classes, to correctly evaluate the relationship between building damage characteristics and SAR imagery. The proposed damage classification is based on Japanese damage assessment data and field photographs, and is classified according to the dominant damage characteristics of the building, such as collapse and damage to walls and roofs. We then analyzed the backscattering characteristics of SAR images for each classified damage class. We used ALOS-2 PALSAR-2 images observed before and after the 2016 Kumamoto earthquake in Mashiki Town, where many buildings were damaged by the earthquake. Then, we performed the analysis using two indices, the correlation coefficient R and the coherence differential value gamma(dif), and the damage class. The results indicate that the backscattering characteristics of SAR images show different trends in each damage class. The R tended to decrease for large deformations such as collapsed buildings. The gamma(dif) was likely to be sensitive not only to collapsed buildings but also to damage with relatively small deformation, such as distortion and tilting. In addition, it was suggested that the ground displacement near the earthquake fault affected the coherence values.

Automated summary

Most research on earthquake-caused building damage extraction using SAR images rely on inaccurate assessment methods. This study proposes a more detailed classification of Major damage buildings based on Japanese assessment data and field photographs. The backscattering characteristics of SAR images were analyzed for each damage class, and it was found that the correlation coefficient R decreased for large deformations such as collapsed buildings, while the coherence differential value gamma(dif) was sensitive to not only collapsed buildings but also damage with relatively small deformation. The study also suggests that ground displacement near the earthquake fault affected the coherence values.