Urban building damage detection from very high resolution imagery by One-Class SVM and shadow information

Abstract

This paper proposed a method that uses shadow change information in bi-temporal images to improve accuracy of urban building damage detection. The initial building damage detection was conducted by object-based bitemporal classification using One-Class Support Vector Machine (OCSVM). The shadow changes extracted from the images were then used to refine the results produced in previous step. The experimental results using bitemporal Quickbird images acquired in Dujiangyan, Sichuan of China showed the proposed method significantly improved the detection accuracy. In particular, the commission error of the building damage was significantly reduced. Further work is required to make more sophisticated rule sets to obtain better results.