Effectiveness of spectral band selection/extraction techniques for spectral data

M Skurichina, S Verzakov, P Paclik, RPW Duin

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

5 Citations (Scopus)


In the past few years a variety of successful algorithms to select/extract discriminative spectral bands was introduced. By exploiting the connectivity of neighbouring spectral bins, these techniques may be more beneficial than the standard feature selection/extraction methods applied for spectral classification. The goal of this paper is to study the effect of the training sample size on the performance of different strategies to select/extract informative spectral regions. We also consider the success of these methods compared to Principal Component Analysis (PCA) for different numbers of extracted components/groups of spectral bands.
Original languageUndefined/Unknown
Title of host publicationStructural, syntactic and statistical pattern recognition
EditorsDY Yeung, JT Kwok, A Fred, F Roli, D de Ridder
Place of PublicationBerlin-Heidelberg
Number of pages10
ISBN (Print)3-540-37236-9
Publication statusPublished - 2006
EventJoint IAPR International Workshops SSPR 2006 and SPR 2006, Hong Kong, China - Heidelberg
Duration: 17 Aug 200619 Aug 2006

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


ConferenceJoint IAPR International Workshops SSPR 2006 and SPR 2006, Hong Kong, China


  • conference contrib. refereed
  • CWTS 0.75 <= JFIS < 2.00

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