Urban building damage detection from very high resolution imagery using OCSVM and spatial features

The availability of commercial very high resolution (VHR) satellite imagery makes it possible to detect and assess building damage in the aftermath of earthquake disasters using these data. Although conventional change detection methods may be used to assess the building damage, the analysis is directed to all classes, both damaged and undamaged, but is not focused on the class of interest. In this paper, we proposed to detect the building damage in urban environments from multitemporal VHR image data using the One-Class Support Vector Machine (OCSVM), a recently developed one-class classifier, which requires training samples from the building damage only. This was illustrated with building damage detection in an urban environment from multitemporal Quickbird images. The detection was conducted at pixel level and object level. Different input vectors for the OCSVM classifier were tested in order to assess the discrimination power of spectral and spatial features: pixel level spectral features and texture features, as well as object-based features. The results showed that the OCSVM performed better on the object level, with an overall accuracy of 82.33% and a kappa coefficient of 60.09%. The results also showed that the OCSVM provides a useful framework that can combine different features and focus on the building damage class of interest. More spatial features are needed to be exploited to obtain more accurate detection results in future studies.