Semi-supervised fuzzy neighborhood preserving analysis for feature extraction in hyperspectral remote sensing images

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dc.authorid0000-0002-0520-9888
dc.contributor.authorAkyürek, Hasan Ali
dc.contributor.authorKoçer, Barış
dc.date.accessioned2020-03-26T20:19:16Z
dc.date.available2020-03-26T20:19:16Z
dc.date.issued2019
dc.departmentSelçuk Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractSemi-supervised feature extraction methods are an important focus of interest in data mining and machine learning areas. These methods are improved methods based on learning from a combination of labeled and unlabeled data. In this study, a semi-supervised feature extraction method called as semi-supervised fuzzy neighborhood preserving analysis (SFNPA) is proposed to improve the classification accuracy of hyperspectral remote sensing images. The proposed method combines the principal component analysis (PCA) method, which is an unsupervised feature extraction method, and the supervised fuzzy neighborhood preserving analysis (FNPA) method and increases the classification accuracy by using a limited number of labeled data. Experimental results on four popular hyperspectral remote sensing datasets show that the proposed method significantly improves classification accuracy on hyperspectral remote sensing images compared to the well-known semi-supervised dimension reduction methods.en_US
dc.identifier.citationAkyürek, H. A., Koçer, B. (2019). Semi-Supervised Fuzzy Neighborhood Preserving Analysis for Feature Extraction in Hyperspectral Remote Sensing Images. Neural Computing and Applications, 31(8), 3385-3415.
dc.identifier.doi10.1007/s00521-017-3279-yen_US
dc.identifier.endpage3415en_US
dc.identifier.issn0941-0643en_US
dc.identifier.issn1433-3058en_US
dc.identifier.issue8en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage3385en_US
dc.identifier.urihttps://dx.doi.org/10.1007/s00521-017-3279-y
dc.identifier.urihttps://hdl.handle.net/20.500.12395/38188
dc.identifier.volume31en_US
dc.identifier.wosWOS:000485922300013en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorKoçer, Barış.
dc.language.isoenen_US
dc.publisherSPRINGER LONDON LTDen_US
dc.relation.ispartofNEURAL COMPUTING & APPLICATIONSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectSemi-supervised feature extractionen_US
dc.subjectHyperspectral image classificationen_US
dc.subjectRemote sensingen_US
dc.subjectFuzzy neighborhood preserving analysisen_US
dc.titleSemi-supervised fuzzy neighborhood preserving analysis for feature extraction in hyperspectral remote sensing imagesen_US
dc.typeArticleen_US

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