Extraction of urban building damage using spectral, height and corner information from VHR satellite images and airborne LiDAR data

Earth observation-based damage assessment has been widely studied in recent years. Considering that the height and spatial variability of buildings change significantly in earthquake-devastated areas, a novel multi-stage urban building damage extraction method that uses bi-temporal spectral, height and corner information is proposed in this study. The post-event height features were directly derived from airborne light detection and ranging (LiDAR) data, whereas pre-event height features were generated using pre-event stereo-paired images from different satellites. The spatial features were quantified using the density of corner points (DCP) in spectral images. The proposed method of urban building damage extraction is summarised as follows. Bi-temporal height and corner features were first generated from bi-temporal very high resolution (VHR) satellite data and post-event airborne LiDAR data. Then, vegetation, bare land (pavement and soil) and shadow were extracted from post-event VHR image and height data, and masked out. Finally, building damage was extracted from the remaining areas using the height difference and DCP difference between pre- and post-event images. A post-processing procedure was used to further refine the initial extraction results. The proposed method was evaluated using bi-temporal VHR images and post-event LiDAR data collected in Port au Prince, Haiti, which was heavily hit by an earthquake in January 2010. The results showed that the proposed method significantly outperformed the two comparative methods in the extraction of urban building damage.