Relevance-Based Feature Extraction for Hyperspectral Images

Abstract--Hyperspectral imagery affords researchers all discriminating details needed for fine delineation of many material classes. This delineation is essential for scientific research ranging from geologic to environmental impact studies. In a data mining scenario, one cannot blindly discard information because it can destroy discovery potential. In a supervised classification scenario, however, the preselection of classes presents one with an opportunity to extract a reduced set of meaningful features without degrading classification performance. Given the complex correlations found in hyperspectral data and the potentially large number of classes, meaningful feature extraction is a difficult task. We turn to the recent neural paradigm of generalized relevance learning vector quantization (GRLVQ) [B. Hammer and T. Villmann, Neural Networks, vol. 15, pp. 1059

• Type: Journal
• Year: 2008
• Where: TNN
• Authors: Michael J. Mendenhall, Erzsébet Merényi
• Keywords: Hyperspectral Data, Meaningful Feature, TNN 2008, Vector Quantization