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

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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Authors and Affiliations

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

   Mathieu Fauvel & Jocelyn Chanussot

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

   Mathieu Fauvel & Jón Atli Benediktsson

Corresponding author

Correspondence to Mathieu Fauvel.

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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