Evaluation of Simulated SAR Images for Building Damage Classification

Synthetic aperture radar (SAR) imagery is invaluable for assessing disaster-induced changes due to its capacity to capture detailed surface information despite varying environmental conditions. However, the scarcity of high-resolution (HR) SAR imagery before disasters presents significant challenges for accurately recognizing changes in damaged buildings, particularly in scenarios requiring pre- and post-disaster image pairs for machine-learning methods that rely on large samples. To address this challenge, our study proposes an innovative solution utilizing simulated SAR imagery generated through ray tracing-based SAR simulation, we generated high-quality predisaster SAR images that closely replicate the scattering properties of authentic SAR imagery using the same sensor orientation as postdisaster image. In this study, to evaluate the feasibility of using simulated SAR images for deep learning-based building damage classification, we investigate three different scenarios: 1) authentic pre- and post-disaster image pairs; 2) simulated predisaster images and authentic postdisaster images; and 3) postdisaster images alone. The methodology was applied in Mashiki, Japan, which was heavily impacted by the 2016 Kumamoto earthquake. Our classification results showed that the simulated predisaster SAR data produced outcomes comparable with those of using authentic image pairs and were clearly superior to the approach that utilized only postevent images. These findings illustrate that simulated SAR imagery is a reliable alternative when authentic predisaster data are unavailable, enabling fast, and accurate damage assessments to support emergency decision-making.