標題: Kernel Nonparametric Weighted Feature Extraction for Hyperspectral Image Classification
作者: Kuo, Bor-Chen
Li, Cheng-Hsuan
Yang, Jinn-Min
電控工程研究所
Institute of Electrical and Control Engineering
關鍵字: Feature extraction;image classification
公開日期: 1-Apr-2009
摘要: In recent years, many studies show that kernel methods are computationally efficient, robust, and stable for pattern analysis. Many kernel-based classifiers were designed and applied to classify remote-sensed data, and some results show that kernel-based classifiers have satisfying performances. Many studies about hyperspectral image classification also show that nonparametric weighted feature extraction (NWFE) is a powerful tool for extracting hyperspectral image features. However, NWFE is still based on linear transformation. In this paper, the kernel method is applied to extend NWFE to kernel-based NWFE (KNWFE). The new KNWFE possesses the advantages of both linear and nonlinear transformation, and the experimental results show that KNWFE outperforms NWFE, decision-boundary feature extraction, independent component analysis, kernel-based principal component analysis, and generalized discriminant analysis.
URI: http://dx.doi.org/10.1109/TGRS.2008.2008308
http://hdl.handle.net/11536/7404
ISSN: 0196-2892
DOI: 10.1109/TGRS.2008.2008308
期刊: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume: 47
Issue: 4
起始頁: 1139
結束頁: 1155
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