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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40564

Title: Adaptive nonparametric weighed feature extraction for hyperspectral image classification
Authors: Bor-Chen Kuo;Shih-Syun Lin;Hsin-Hua Ho;Jinn-Min Yang
Contributors: 國立臺灣海洋大學:資訊工程學系
Date: 2009
Issue Date: 2017-01-20T02:31:24Z
Publisher: Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009. WHISPERS '09. First Workshop on
Abstract: Abstract: In this study, a novel classifier ensemble method named adaptive nonparametric weighted feature extraction (AdaNWFE) is proposed. This new concept is deduced from AdaBoost and NWFE. The main idea of AdaNWFE is adaptive in the sense that subsequent feature spaces are tweaked in favor of those instances misclassified by classifiers in the previous feature space. All training samples are projected to these feature spaces to train various classifiers and then constitute a multiple classifier system. The experimental results based on two hyperspectral data sets show that the proposed algorithm can generate better classification results than only applying NWFE.
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40564
Appears in Collections:[資訊工程學系] 演講及研討會

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