6533b853fe1ef96bd12aca9f

RESEARCH PRODUCT

Feature extraction from remote sensing data using Kernel Orthonormalized PLS

Gustau Camps-vallsJeronimo Arenas-garcia

subject

business.industryComputer scienceFeature extractionContext (language use)Regression analysisPattern recognitionSparse approximationcomputer.software_genreKernel principal component analysisKernel (linear algebra)Kernel embedding of distributionsKernel (statistics)Radial basis function kernelArtificial intelligenceData miningbusinesscomputerRemote sensing

description

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.

yearjournalcountryeditionlanguage
2007-01-012007 IEEE International Geoscience and Remote Sensing Symposium
https://doi.org/10.1109/igarss.2007.4422779