Feature extraction from remote sensing data using Kernel Orthonormalized PLS

Author

Arenas-García, Jerónimo ; Camps-Valls, Gustavo

Author_Institution

Univ. Carlos III de Madrid, Madrid

fYear

2007

fDate

23-28 July 2007

Firstpage

258

Lastpage

261

Abstract

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 orthonormalized 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.

Keywords

feature extraction; geophysical signal processing; geophysical techniques; least squares approximations; regression analysis; remote sensing; signal classification; kernel orthonormalized PLS algorithm; nonlinear feature extraction; regression problem; remote sensing classification; remote sensing data; sparse approximation; sparse kernel-based method; Approximation algorithms; Covariance matrix; Data mining; Feature extraction; Kernel; Large-scale systems; Least squares approximation; Least squares methods; Remote sensing; Scalability;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International

Conference_Location

Barcelona

Print_ISBN

978-1-4244-1211-2

Electronic_ISBN

978-1-4244-1212-9

Type

conf

DOI

10.1109/IGARSS.2007.4422779

Filename

4422779