Parameter optimization in the regularized kernel minimum noise fraction transformation

Allan Aasbjerg Nielsen (Invited author), Jacob Schack Vestergaard (Invited author)

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Abstract

Based on the original, linear minimum noise fraction (MNF) transformation and kernel principal component analysis, a kernel version of the MNF transformation was recently introduced. Inspired by we here give a simple method for finding optimal parameters in a regularized version of kernel MNF analysis. We consider the model signal-to-noise ratio (SNR) as a function of the kernel parameters and the regularization parameter. In 2-4 steps of increasingly refined grid searches we find the parameters that maximize the model SNR. An example based on data from the DLR 3K camera system is given.
Original languageEnglish
Title of host publicationIEEE International Geoscience and Remote Sensing Symposium proceedings
PublisherIEEE
Publication date2012
Pages370-373
ISBN (Print)978-1-4673-1160-1
ISBN (Electronic)978-1-4673-1158-8
DOIs
Publication statusPublished - 2012
Event2012 IEEE International Geoscience and Remote Sensing Symposium: Remote Sensing for a Dynamic Earth - Munich, Germany
Duration: 22 Jul 201227 Jul 2012
https://ieeexplore.ieee.org/xpl/conhome/6334512/proceeding

Conference

Conference2012 IEEE International Geoscience and Remote Sensing Symposium
Country/TerritoryGermany
CityMunich
Period22/07/201227/07/2012
Internet address
SeriesIEEE International Geoscience and Remote Sensing Symposium
ISSN2153-6996

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