A New Method for the Identification of Earthquake-Damaged Buildings Using Sentinel-1 Multitemporal 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 full-time 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 postearthquake building damage proxy map (BDPM) based on multitemporal 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
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6.2 Jishishan, China earthquake. We also verified its accuracy (over 80%) by comparing the BDPM with results from the United Nations Institute for Training and Research and analyzed various factors affecting the distribution of damaged buildings. These analyses confirm the effectiveness of our method for generating BDPM using free medium-resolution Sentinel-1 data, which will greatly assist in earthquake emergency response.