In: Wagner W., Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
URBAN IMPERVIOUS SURFACE EXTRACTION FROM VERY HIGH RESOLUTION 
IMAGERY BY ONE-CLASS SUPPORT VECTOR MACHINE 
P. Li, H. Xu, S. Li 
Institute of Remote Sensing and GIS, School of Earth and Space Sciences, Peking University, Beijing 100871, P R 
China - pjli@pku.edu.cn 
KEY WORDS: impervious surface, multi-level segmentation, One-Class SVM, very high resolution imagery, land cover 
classification 
ABSTRACT: 
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. 
1. INTRODUCTION 
Impervious surface is defined as any materials that water 
cannot infiltrate, and has been recognized as an important 
indicator in urban environmental assessment and valuable input 
to planning and management activities (Lu and Weng, 2009; 
Yuan and Bauer, 2006). The extraction of impervious surface 
from remote sensing imagery has continued to be an important 
problem for more than three decades. In recent years, the 
increasing availability of very high resolution (VHR) imagery, 
such as IKONOS, Quickbird and GeoEye-1, provides great 
opportunity for detailed impervious surface mapping in urban 
areas. Although some methods using VHR images have been 
developed (Lu and Weng, 2009; Yuan and Bauer, 2006; Goetz 
et al., 2003; Cablk and Minor, 2003; Zhou and Wang, 2008; 
Roeck et al., 2009), obtaining highly accurate land cover and 
impervious surface information from VHR imagery remains 
challenging, thus new methods and techniques are still required. 
However, since there is extensive occurrence of shadows in 
VHR imagery caused by high buildings and trees in dense 
urban areas, which leads to the reduced or total loss of spectral 
information in the shaded areas, an important problem to be 
addressed is to identify the impervious surfaces in shaded areas 
(Lu and Weng, 2009). As in general land cover classification of 
urban areas using VHR images, object based methods are also 
commonly used to extract impervious surfaces (Cablk and 
Minor, 2003; Yuan and Bauer, 2006; Zhou and Wang, 2008). 
2. METHODS 
In this study, we adopted a two-stage object based method to 
extract impervious surface. At the first stage, shadow areas 
were identified at object level generated by image segmentation. 
At the second stage, shadow areas and non-shadow areas were 
separately classified to extract impervious surface, using 
one-class Support Vector Machine (One-class SVM or 
OCSVM). 
Prior to these two stages, multilevel hierarchical segmentation 
using the proposed method was first carried out, different levels 
of segmentation results were then selected for each stage. For 
example, since shadow extraction at the first stage and shadow 
classification at the second stage require different levels of 
segmentation detail, shadow extraction was conducted at a 
coarse level of segmentation, whereas the shadow classification