Combining clustering and classification for remote-sensing images using unlabeled data  

作　　者：边小勇 张天序 张晓龙 

机构地区：[1]Institute for Pattern Recognition and Artificial Intelligence,Huazhong University of Science and Technology [2]School of Computer Science and Technology,Wuhan University of Science and Technology

出　　处：《Chinese Optics Letters》2011年第1期41-44,共4页中国光学快报（英文版）

基　　金：supported by the National Natural Science Foundation of China (Nos.60736010 and 60975031);in part by the Science Foundation of Wuhan University of Science and Technology (No.2008TD04);the Open Foundation of State Key Laboratory of Bioelectronics,Southeast University;Hubei Provincial Natural Science Foundation (No.2009CAD034)

摘　　要：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.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.

关 键 词：Remote sensing Support vector machines 

分 类 号：P237[天文地球—摄影测量与遥感]