Title :
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
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;
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
DOI :
10.1109/IGARSS.2007.4422779
