A New Method for The Identification of Earthquake-damaged Buildings Using Sentinel-1 Multi-temporal Coherence Optimized by Homogeneous SAR Pixels and Histogram Matching

Emergency response after earthquakes, especially rapid access to building damage information, is of great significance to ensure timely rescue and reduce casualties.However, manual field surveys of building damages are inefficient and dangerous, and optical satellite data are more susceptible to cloud interference after earthquakes.Synthetic Aperture Radar (SAR) is now widely used in disaster response efforts due to its fulltime and all-weather capability.According to the change in coherence between SAR images before and after earthquakes, it is possible to identify damaged buildings that cause coherence loss.However, the accuracy of traditional coherence-based damage detection methods is relatively low due to biases in coherence estimation and inconsistency in spatio-temporal baselines.In this study, we propose a new method to produce a post-earthquake Building Damage Proxy Map (BDPM) based on multi-temporal Sentinel-1 coherence, which incorporates homogeneous SAR pixel coherence estimation and histogram matching techniques.The former is used to reduce estimation biases and the latter to reduce the effect of baseline inconsistencies in adjacent coherence maps.We successfully applied this method to the 2022 Mw 6.2 Afghanistan earthquake, the 2023 strong earthquake sequence in Turkey, and the 2023 Ms 6.2 Jishishan, China earthquake.We also verified its accuracy (over 80%) by comparing the BDPM with