Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kuo, Bor-Chen | en_US |
dc.contributor.author | Li, Cheng-Hsuan | en_US |
dc.contributor.author | Yang, Jinn-Min | en_US |
dc.date.accessioned | 2014-12-08T15:09:41Z | - |
dc.date.available | 2014-12-08T15:09:41Z | - |
dc.date.issued | 2009-04-01 | en_US |
dc.identifier.issn | 0196-2892 | en_US |
dc.identifier.uri | http://dx.doi.org/10.1109/TGRS.2008.2008308 | en_US |
dc.identifier.uri | http://hdl.handle.net/11536/7404 | - |
dc.description.abstract | 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. | en_US |
dc.language.iso | en_US | en_US |
dc.subject | Feature extraction | en_US |
dc.subject | image classification | en_US |
dc.title | Kernel Nonparametric Weighted Feature Extraction for Hyperspectral Image Classification | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1109/TGRS.2008.2008308 | en_US |
dc.identifier.journal | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | en_US |
dc.citation.volume | 47 | en_US |
dc.citation.issue | 4 | en_US |
dc.citation.spage | 1139 | en_US |
dc.citation.epage | 1155 | en_US |
dc.contributor.department | 電控工程研究所 | zh_TW |
dc.contributor.department | Institute of Electrical and Control Engineering | en_US |
dc.identifier.wosnumber | WOS:000264630200015 | - |
dc.citation.woscount | 37 | - |
Appears in Collections: | Articles |
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