Combining clustering and classification for remote-sensing images using unlabeled data
Xiaoyong Bian, 张天序 Tianxu Zhang, 张晓龙 Xiaolong Zhang · Chinese Optics Letters · 2011
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.