Regularized feature extractions and support vector machines for hyperspectral image data classification

Authors:
Bor-Chen Kuo;Kuang-Yu Chang
Affiliations:
Graduate School of Educational Measurement and Statistics, National Taichung Teachers College, Taichung, Taiwan;Graduate School of Educational Measurement and Statistics, National Taichung Teachers College, Taichung, Taiwan
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Year:
2005

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Abstract

In this study, the performances of using parametric/ nonparametric regularized feature extractions and support vector machine for hyperspectral image classification is explored when the training sample size is small. The classification accuracies of RBF-based SVM using two feature extractions with three regularization techniques are evaluated. The results of two hyperspectral image classification experiments show that the performance of the combination of nonparametric weighted feature extraction and RBF-based SVM outperforms those of others.