SVDD-based one-class land-cover mapping using optimal training samples

Remotely sensed data have been widely used in the field of producing land-cover thematic maps. When dealing with single class problem, one-class classifiers proved to be more effective compared with conventional supervised classifiers. The Support Vector Data Description (SVDD), one kind of one-class classification method, has been applied to specific land-cover classifications lately. However, the sampling scheme used in previous studies does not follow the SVDD principle. In this paper, Euclidean distance and Mahalanobis distance were chosen as an index to optimize training samples in order to improve the accuracy of SVDD classification. Result shows that sample optimization do improve the classification accuracy. Besides, compared with the Euclidean distance, Mahalanobis distance is more suitable and effective for sample optimization.