Open Access

Kernel Principal Component Analysis for the Classification of Hyperspectral Remote Sensing Data over Urban Areas

  • Mathieu Fauvel1, 2Email author,
  • Jocelyn Chanussot1 and
  • Jón Atli Benediktsson2
EURASIP Journal on Advances in Signal Processing20092009:783194

Received: 2 September 2008

Accepted: 4 February 2009

Published: 22 March 2009


Kernel principal component analysis (KPCA) is investigated for feature extraction from hyperspectral remote sensing data. Features extracted using KPCA are classified using linear support vector machines. In one experiment, it is shown that kernel principal component features are more linearly separable than features extracted with conventional principal component analysis. In a second experiment, kernel principal components are used to construct the extended morphological profile (EMP). Classification results, in terms of accuracy, are improved in comparison to original approach which used conventional principal component analysis for constructing the EMP. Experimental results presented in this paper confirm the usefulness of the KPCA for the analysis of hyperspectral data. For the one data set, the overall classification accuracy increases from 79% to 96% with the proposed approach.


Support Vector MachineFeature ExtractionRemote SensingClassification AccuracySensing Data

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Authors’ Affiliations

GIPSA-lab, Grenoble INP, Saint Martin d'Hères, France
Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland


© Mathieu Fauvel et al. 2009

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.