Integrating Object-based Classification with One-class Support Vector Machines in Mapping a Specific Land Class from High Spatial Resolution Images

Remote sensing techniques have been commonly used to map land cover and land use types. For many applications, users may only be interested in a specific land class in an image such as extracting urban areas from an image, or retrieving dead trees from a forest. This could be referred to as a one-class classification problem. In addition, with the increasing availability of high spatial resolution imagery, earth objects can be mapped in detail, which enable us to quickly update and monitor the change of a specific class. However, conventional pixel-based classification methods have difficulty in dealing with high spatial resolution remote sensing data. In this study, we use urban house extraction as an example, and propose to classify houses from high spatial resolution images by integrating one-class Support Vector Machines (SVMs) and object-based classifiers. We also compared the performance from the proposed method with the one-class SVMs and pixel-based method. The results indicate that the proposed method outperforms the pixel based method, and could be a promising way to provide relatively quick and efficient way in extracting a specific land class from high spatial resolution images.