?标题:A Novel Hierarchical Semisupervised SVM for Classification of Hyperspectral Images

作者:Shao, ZF (Shao, Zhenfeng); Zhang, L (Zhang, Lei); Zhou, XR (Zhou, Xiran); Ding, L (Ding, Lin)

来源出版物:IEEE GEOSCIENCE AND REMOTE SENSING LETTERS卷:11期:9页:1609-1613DOI:10.1109/LGRS.2014.2302034出版年:SEP 2014

Web of Science 核心合集中的 "被引频次":0

被引频次合计:0

摘要:This letter presents a novel hierarchical semisupervised support vector machine (SVM) for classification of hyperspectral images. The method exploits the wealth of unlabeled samples by means of their cluster features. The method learns a suitable framework for classifying cluster features by a semisupervised SVM and thus makes use of advantages of clustering and classification. Experimental results demonstrate that the proposed classification method is effective for hyperspectral image classification when a few labeled samples are available. Another advantage of the proposed method is that the hierarchical structure can simultaneously take clustering and classification information into consideration.

地址:[Shao, Zhenfeng; Zhang, Lei; Zhou, Xiran; Ding, Lin] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China.

通讯作者地址:Shao, ZF (通讯作者),Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China.

电子邮件地址:shaozhenfeng@whu.edu.cn; xysyleilei@126.com; zhouxiranjuven@163.com; dinglin@whu.edu.cn