Explicit signal to noise ratio in reproducing kernel Hilbert spaces

Luis Gomez-Chova, Allan Aasbjerg Nielsen, Gustavo Camps-Valls

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

This paper introduces a nonlinear feature extraction method based on kernels for remote sensing data analysis. The proposed approach is based on the minimum noise fraction (MNF) transform, which maximizes the signal variance while also minimizing the estimated noise variance. We here propose an alternative kernel MNF (KMNF) in which the noise is explicitly estimated in the reproducing kernel Hilbert space. This enables KMNF dealing with non-linear relations between the noise and the signal features jointly. Results show that the proposed KMNF provides the most noise-free features when confronted with PCA, MNF, KPCA, and the previous version of KMNF. Extracted features with the explicit KMNF also improve hyperspectral image classification.
Original languageEnglish
Title of host publicationIEEE International Geoscience and Remote Sensing Symposium Proceedings (IGARSS)
PublisherIEEE
Publication date2011
Pages3570-3573
ISBN (Print)978-1-4577-1003-2
DOIs
Publication statusPublished - 2011
Event2011 IEEE International Geoscience and Remote Sensing Symposium - Vancouver, Canada
Duration: 24 Jul 201129 Jul 2011
http://igarss11.org/

Conference

Conference2011 IEEE International Geoscience and Remote Sensing Symposium
Country/TerritoryCanada
CityVancouver
Period24/07/201129/07/2011
Internet address

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