Open Access Research Article

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

Mathieu Fauvel12*, Jocelyn Chanussot1 and Jón Atli Benediktsson2

Author Affiliations

1 GIPSA-lab, Grenoble INP, BP 46, 38402 Saint Martin d'Hères, France

2 Faculty of Electrical and Computer Engineering, University of Iceland, Hjardarhagi 2-6, 107 Reykjavik, Iceland

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EURASIP Journal on Advances in Signal Processing 2009, 2009:783194  doi:10.1155/2009/783194

The electronic version of this article is the complete one and can be found online at:

Received: 2 September 2008
Revisions received: 19 December 2008
Accepted: 4 February 2009
Published: 22 March 2009

© 2009 The Author(s).

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.


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.

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