Classification of high dimensional and imbalanced hyperspectral imagery data

Authors:
Vicente García;J. Salvador Sánchez;Ramón A. Mollineda
Affiliations:
Institute of New Imaging Technologies, Department of Computer Languages and Systems, Universitat Jaume I, Castelló de la Plana, Spain;Institute of New Imaging Technologies, Department of Computer Languages and Systems, Universitat Jaume I, Castelló de la Plana, Spain;Institute of New Imaging Technologies, Department of Computer Languages and Systems, Universitat Jaume I, Castelló de la Plana, Spain
Venue:
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Year:
2011

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Abstract

The present paper addresses the problem of the classification of hyperspectral images with multiple imbalanced classes and very high dimensionality. Class imbalance is handled by resampling the data set, whereas PCA is applied to reduce the number of spectral bands. This is a preliminary study that pursues to investigate the benefits of using together these two techniques, and also to evaluate the application order that leads to the best classification performance. Experimental results demonstrate the significance of combining these preprocessing tools to improve the performance of hyperspectral imagery classification. Although it seems that the most effective order of application corresponds to first a resampling algorithm and then PCA, this is a question that still needs a much more thorough investigation.