Full metadata record
DC FieldValueLanguage
dc.contributor.authorKuo, Bor-Chenen_US
dc.contributor.authorLi, Cheng-Hsuanen_US
dc.contributor.authorYang, Jinn-Minen_US
dc.date.accessioned2014-12-08T15:09:41Z-
dc.date.available2014-12-08T15:09:41Z-
dc.date.issued2009-04-01en_US
dc.identifier.issn0196-2892en_US
dc.identifier.urihttp://dx.doi.org/10.1109/TGRS.2008.2008308en_US
dc.identifier.urihttp://hdl.handle.net/11536/7404-
dc.description.abstractIn 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.isoen_USen_US
dc.subjectFeature extractionen_US
dc.subjectimage classificationen_US
dc.titleKernel Nonparametric Weighted Feature Extraction for Hyperspectral Image Classificationen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/TGRS.2008.2008308en_US
dc.identifier.journalIEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSINGen_US
dc.citation.volume47en_US
dc.citation.issue4en_US
dc.citation.spage1139en_US
dc.citation.epage1155en_US
dc.contributor.department電控工程研究所zh_TW
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.identifier.wosnumberWOS:000264630200015-
dc.citation.woscount37-
Appears in Collections:Articles


Files in This Item:

  1. 000264630200015.pdf