A joint clustering and classification approach is proposed. This approach exploits unlabeled data for efficient clustering, which is applied in the classification with support vector machine (SVM) in the case of small-size training samples. The proposed method requires no prior information on data labels, and yields better cluster structures. Through cluster assumption and the notions of support vectors, the most confident k cluster centers and data points near the cluster boundaries are labeled and used to train a reliable SVM classifier. Our method gains better estimation of data distributions and mitigates the unrepresentative problem of small-size training samples. The data set collected from Landsat Thematic Mapper (Landsat TM-5) validates the effectiveness of the proposed approach.
© 2011 Chinese Optics Letters
Xiaoyong Bian, Tianxu Zhang, and Xiaolong Zhang, "Combining clustering and classification for remote-sensing images using unlabeled data," Chin. Opt. Lett. 9, 011002- (2011)