Fusion of vertical and oblique images using Intra-Cluster-Classification for building damage assessment

Abstract

Using High Resolution (HR) and Very High Resolution (VHR) Remote Sensing (RS) images for post-disaster building damage assessment provides more information than low-resolution images. Consequently, a damage map is expected to be at building level, which requires both rooftop and facade information. Oblique imagery is therefore becoming increasingly popular for post-disaster analysis. However, oblique images are rarely available in the pre-disaster acquisition, and thus without any prior information it is a challenging task to assess the facade damage just from a post-disaster scene. As a solution, we aggregated information at the cluster level using pre-disaster neighborhood buildings’ feature analysis, and thus the post-disaster building-level damage assessment is supported by the cluster-level information. Therefore, the proposed method, Intra-Cluster-Classification (ICC), uses hierarchical steps of unsupervised and supervised methods to detect damaged and undamaged areas within each cluster of buildings. The procedure is implemented on Google Earth Engine platform, and the results are evaluated using Hurricane Michael (2018) images. At the building-level, damage information is shown as a fractional number between 0 and 1, with the higher number indicating more destruction. R-squared (R2) value is 0.9688 between actual and predicted damage scores. In addition, the Overall Accuracy (OA) and the Kappa coefficient (K) in the 4-class RS-scale are 83.2% and 0.7438, respectively. Furthermore, in 3-class RS-scale, the OA and K of our results are 91.08%, and 0.8582, respectively.