Parameter optimization in the regularized kernel minimum noise fraction transformation

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2012

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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
StatePublished

Conference

ConferenceIEEE International Geoscience and Remote Sensing Symposium (IGARSS 2012)
Number2012
CountryGermany
CityMünich
Period22/07/1227/07/12
Internet addresshttp://www.igarss2012.org/
NameIEEE International Geoscience and Remote Sensing Symposium
ISSN (Print)2153-6996
CitationsWeb of Science® Times Cited: No match on DOI
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