Feature extraction from remote sensing data using kernel orthonormalized PLS

被引：8

作者：

Arenas-Garcia, Jeronimo ^{[1
]}

Camps-Valls, Gustavo ^{[2
]}

机构：

[1] Univ Carlos III Madrid, Dept Signal Theory & Commun, E-28903 Getafe, Spain

[2] Univ Valencia, Dept Elect Engn, E-46003 Valencia, Spain

来源：

IGARSS: 2007 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-12: SENSING AND UNDERSTANDING OUR PLANET | 2007年

关键词：

D O I：

10.1109/IGARSS.2007.4422779

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：

This paper presents the study of a sparse kernel-based method for non-linear feature extraction in the context of remote sensing classification and regression problems. The so-called Kernel Orthonomalized PLS algorithm with reduced complexity (rKOPLS) has two core parts: (i) a kernel version of OPLS (called KOPLS), and (ii) a sparse (reduced) approximation for large scale data sets, which ultimately leads to rKOPLS. The method demonstrates good capabilities in terms of expressive power of the extracted features and scalability.

引用

页码：258A / +

页数：2

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