Effectiveness of Spectral Band Selection/Extraction Techniques for Spectral Data

  • Marina Skurichina
  • Sergey Verzakov
  • Pavel Paclik
  • Robert P. W. Duin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4109)


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.


Principal Component Analysis Spectral Region Spectral Band Data Class Sequential Selection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Marina Skurichina
    • 1
  • Sergey Verzakov
    • 1
  • Pavel Paclik
    • 1
  • Robert P. W. Duin
    • 1
  1. 1.Information and Communication Theory Group, Faculty of Electrical Engineering, Mathematics and Computer ScienceDelft University of TechnologyDelftThe Netherlands

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