Data-dependent semi-supervised hyperspectral image classification

Haobo Lv, Xiaoqiang Lu, Yuan Yuan

科研成果: 书/报告/会议事项章节会议稿件同行评审

4 引用 (Scopus)

摘要

Hyperspectral imagery provides more powerful information than multispectral remote sensing data. However, when hyperspectral data is used for classification task, the highdimension features often lead to ill-conditioned problems, such as the Hughes phenomenon. To tackle this problem, various supervised dimensional reduction methods are proposed. However, these methods only exploit the labeled training data and ignore the huge unlabelled data. To utilize the unlabelled data space structure information in dimension reduction, a method is proposed as Data-dependent semi-supervised (DDSS). The proposed method exploits the space structure of labeled data and unlabelled data jointly to reduce the dimensionality of the image cures. Experimental results show that this method significantly outperforms the state-of-the-art dimension reduction methods for classification and denoising.

源语言英语
主期刊名2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Proceedings
664-668
页数5
DOI
出版状态已出版 - 2013
已对外发布
活动2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Beijing, 中国
期限: 6 7月 201310 7月 2013

出版系列

姓名2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Proceedings

会议

会议2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013
国家/地区中国
Beijing
时期6/07/1310/07/13

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