An optimal nonparametric weighted system for hyperspectral data classification

Authors:
Li-Wei Ko;Bor-Chen Kuo;Ching-Teng Lin
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;Department of Electrical and Control Engineering, National Chiao-Tung University, Hsinchu, 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 real situation, gathering enough training samples is difficult and expensive. Assumption of enough training samples is usually not satisfied for high dimensional data. Small training sets usually cause Hughes phenomenon and singularity problems. Feature extraction and feature selection are usual ways to overcome these problems. In this study, an optimal classification system for classifying hyperspectral image data is proposed. It is made up of orthonormal coordinate axes of the feature space. Classification performance of the classification system is much better than the other well-known ones according to the experiment results below. It possesses the advantage of using fewer features and getting better performance.