An Analysis of Dimension Reduction Methods Applicable for Out Of Sample Problem in Hyperspectral Images

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dc.contributor.authorYilmaz, Atinc
dc.contributor.authorOzturk, Umit
dc.date.accessioned2024-03-13T10:30:29Z
dc.date.available2024-03-13T10:30:29Z
dc.date.issued2019
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description9th International Conference on Recent Advances in Space Technologies (RAST) -- JUN 11-14, 2019 -- Istanbul, TURKEYen_US
dc.description.abstractDimension reduction (DR) techniques provide advantages in terms of reducing computational time and data storage requirements. These methods are widely used for Hyper-spectral Imaging (HSI) data classification purposes. However, the need of extention to out of sample (OOS) data during the learning procedure is a main drawback for traditional manifold learning (ML) algorithms. There are a number of DR methods applicable for OOS problem. This study aims at focusing a comprehensive analysis of these DR methods with HSI data classification. In this context, we made experiments on HSI data by applying LPP, NPE, OLPP, ONPE and Nystrom (for LE and LLE) algorithms to observe classification performance. Firstly, parametric graphs are obtained to understand the behaviour and then, classification performance is measured upon reduced dimensionality. Support Vector Machines (SVM) and K-Nearest Neighbor (KNN) are used as classifier. All experiments are conducted on Kennedy Space Center (KSC) and Indian Pines hyperspectral data.en_US
dc.identifier.doi10.1109/rast.2019.8767826
dc.identifier.endpage386en_US
dc.identifier.isbn978-1-5386-9448-0
dc.identifier.scopus2-s2.0-85073913908en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage381en_US
dc.identifier.urihttps://doi.org/10.1109/rast.2019.8767826
dc.identifier.urihttps://hdl.handle.net/20.500.12662/3368
dc.identifier.wosWOS:000492052500060en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2019 9th International Conference On Recent Advances In Space Technologies (Rast)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectdimension reductionen_US
dc.subjecthyperspectral imagingen_US
dc.subjectclassificationen_US
dc.titleAn Analysis of Dimension Reduction Methods Applicable for Out Of Sample Problem in Hyperspectral Imagesen_US
dc.typeConference Objecten_US

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