Urban Impervious Surface Extraction from Very High Resolution Imagery by One-class Support Vector Machine

This paper proposes a new method for extracting impervious surface from VHR imagery. Since the impervious surface is the only class of interest (i.e. target class), the One Class Support Vector Machine (OCSVM), a recently developed statistical learning method, was used as the classifier. Rather than use samples from all classes for training in traditional multi-class classification, the method only requires samples of the target class for training. The classification was conducted on object level. The proposed method was evaluated and compared to existing methods using Quickbird image from Beijing urban area. The results showed that the proposed method outperformed the existing method in term of classification accuracy. The method provides an effective way to extract impervious surface from VHR images.