Land-Cover Change Detection using One-Class Support Vector Machine

Change detection using remote sensing has considerable potential for monitoring land-cover change. Commonly, one specific class of change is of interest in many applications. In this paper, a recently developed one-class classifier, the One-Class Support Vector Machine (OCSVM), is proposed for the change detection of one specific class by multitemporal classification. The classifier only requires samples from the change class of interest as the training data. The performance of the proposed method was evaluated in two applications by comparing with conventional post-classification comparison methods. The results demonstrated the proposed method achieved both higher overall accuracy and higher kappa coefficient than the conventional methods, and demonstrated good potential for further application. The study also indicated that with the OCVSM, the analysis can focus only on the specific class of interest and does not need to treat other classes, thus providing highly accurate change detection. The OCSVM-based change detection method, as a general and easily implemented method, can be used for applications where only the change of one specific class is of interest.