A Module for Enhancing Accuracy of Building Damage Detection by Fusing Features from Pre and Post Disaster Remote Sensing Images
Xuanchao Fu, Toru Kouyama, Wenhao Shen, Suomi Seki, Ryosuke Nakamura, Ichiro Yoshikawa · 2023
In the aftermath of large-scale natural disasters, the accuracy of building damage detection (BDD) is of critical importance. Post-disaster high-resolution (post-HR) remote sensing imagery is fundamental for BDD; however, prompt acquisition of such imagery remains a significant challenge. To address this issue, we introduce a novel plug-and-play feature fusion (FF) module. This module, strategically situated between a pre-trained super-resolution (SR) model and a BDD model, ingeniously combines features from both pre-disaster high-resolution (pre-HR) and super-resolved post-disaster remote sensing imagery. The proposed approach is designed to maximize the utilization of pre-HR images, thereby enhancing BDD accuracy. Experimental validation confirms that this improvement in accuracy is attributable to the pragmatic extraction of features from pre-HR imagery, not just an increase in model complexity. Consequently, our approach holds substantial promise for real-world post-disaster scenarios and lays a solid foundation for future BDD research, demonstrating potential improvements in both efficacy and practicality.