A novel remote sensing detection method for buildings damaged by earthquake based on multiscale adaptive multiple feature fusion

The rapid and accurate detection of damaged buildings after an earthquake are critical for emergency response. Given the difference in the textures of damaged parts and those of the original buildings, damaged buildings can be accurately detected through textural heterogeneity. However, quantitatively detecting damaged buildings using such heterogeneity from post-earthquake images is difficult. Therefore, we propose a method of automatically extracting house damage information from post-quake high-resolution optical remote sensing imagery through the multiscale fusion of spectral and textural features, which can be achieved in three steps. Firstly, the textural and spectral features of the images are enhanced at the pixel level. Secondly, the resulting feature images are fused at the feature level and the fused feature images are segmented using superpixels. Lastly, a post-quake house damage index model is constructed. Results show an overall accuracy of 76.75%, 75.35% and 83.25% for three different types of imagery. This finding indicates that the proposed algorithm can be used to extract damage information from multisource remote sensing data and provide useful guidance for post-disaster rescue and assessment based on regional house damage conditions.