Effectiveness of Spectral Band Selection/Extraction Techniques for Spectral Data (original) (raw)

Abstract

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.

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Authors and Affiliations

  1. Information and Communication Theory Group, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, P.O. Box 5031, 2600, GA, Delft, The Netherlands
    Marina Skurichina, Sergey Verzakov, Pavel Paclik & Robert P. W. Duin

Authors

  1. Marina Skurichina
  2. Sergey Verzakov
  3. Pavel Paclik
  4. Robert P. W. Duin

Editor information

Editors and Affiliations

  1. Hong Kong University of Science and Technology,
    Dit-Yan Yeung
  2. Department of Computer Science, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
    James T. Kwok
  3. Instituto de Telecomunicações, Instituto Superior Técnico, Lisbon, Portugal
    Ana Fred
  4. Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, 09123, Cagliari, Italy
    Fabio Roli
  5. Faculty of Electrical Engineering, Mathematics and Computer Science, Information and Communication Theory Group, Delft University of Technology, Delft, The Netherlands
    Dick de Ridder

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© 2006 Springer-Verlag Berlin Heidelberg

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Skurichina, M., Verzakov, S., Paclik, P., Duin, R.P.W. (2006). Effectiveness of Spectral Band Selection/Extraction Techniques for Spectral Data. In: Yeung, DY., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2006. Lecture Notes in Computer Science, vol 4109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11815921\_59

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