SEMI-SUPERVISED DIMENSIONALITY REDUCTION FOR HYPERSPECTRAL REMOTE SENSING IMAGE CLASSIFICATION

被引：0
|
作者：
Xia, Junshi [1 ,3 ]
Chanussot, Jocelyn [1 ]
Du, Peijun [2 ,3 ]
He, Xiyan [1 ]
机构：
[1] Domaine Univ, Grenoble Inst Technol, GIPSA Lab, BP 46, F-38402 St Martin Dheres, France
[2] Nanjing Univ, Dept Geog Informat Sci, Nanjing 210093, Jiangsu, Peoples R China
[3] China Univ Min & Technol, Key Lab Land Environm & Disaster Monitoring SBSM, Xuzhou 221116, Jiangsu, Peoples R China
来源：
2012 4TH WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING (WHISPERS) | 2012年
基金：
中国国家自然科学基金;
关键词：
Semi-supervised; Dimensionality reduction; hyperspectral remote sensing; classification;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Class labels and pairwise constraints are adopted as the prior information to present the semi-supervised dimensionality reduction for hyperspectral image. In this paper, we extend semi-supervised probabilistic principal component analysis (S(2)PPCA), semi-supervised local fisher discriminant analysis (S(2)LFDA) and semi-supervised dimensionality reduction with pairwise constraints (S(2)DRpc) to extract the features of hyperspectral image. These semi-supervised dimensionality reduction approaches are compared with PCA in classification task. Experimental results show that semi-supervised algorithms of S(2)PPCA and S(2)DRpc are superior to PCA.
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页数：4
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