Application of Remote Sensing Image Change Detection Algorithm in Extracting Damaged Buildings in Earthquake Disaster

In post-earthquake reconstruction, the rapid and effective utilization of pre-disaster and post-disaster remote sensing data is crucial. In such scenarios, remote sensing satellite technology demonstrates its unique advantages by quickly and dynamically acquiring high-resolution imagery of extensive areas in earthquake-affected regions, efficiently capturing the immediate post-disaster situation. Change detection technology has become a key tool through these remote sensing images. This technology can automatically identify changes in damaged areas, buildings, and other critical infrastructure through the analysis of pre-disaster and post-disaster remote sensing imagery data, aiding in post-disaster reconstruction efforts. Existing remote sensing change detection methods mainly rely on Convolutional Neural Network (CNN) or Transformer for construction. Still, these methods often fail to fully balance the advantages and disadvantages of these two technologies. They are not specifically optimized for the features of the change detection task (extracting and learning features of the changed area). To address this issue, this paper fully leverages the global information processing capabilities of the Transformer and the local information capture capabilities of CNN, proposing a multi-level feature guided aggregation network model composed of multiple branches that fully integrate the respective strengths of both. The model initially captures global information from the images using the Transformer-based main network. Subsequently, it extracts local information from the images employing a custom multi-scale strip convolution module based on CNN. Subsequently, the global and local information extracted during the encoding phase is further integrated through the feature aggregation network, and the final prediction map is generated using an attention fusion module. In the experimental section, the effectiveness of the proposed algorithm is further validated through comparative experiments conducted on multiple publicly available datasets.