An efficient feature extraction approach for hyperspectral images using Wavelet High Dimensional Model Representation

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dc.contributor.authorTuna, Suha
dc.contributor.authorOzay, Evrim Korkmaz
dc.contributor.authorTunga, Burcu
dc.contributor.authorGurvit, Ercan
dc.contributor.authorTunga, M. Alper
dc.date.accessioned2024-03-13T10:35:20Z
dc.date.available2024-03-13T10:35:20Z
dc.date.issued2022
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description.abstractHyperspectral (HS) Imagery helps to capture information using specialized sensors to extract detailed data at numerous narrow wavelengths. Hyperspectral imaging provides both spatial and spectral characteristics of regions or objects for subsequent analysis. Unfortunately, various noise sources decrease the interpretability of these images as well as the correlation between neighbouring pixels, hence both reduce the classification performance. This study focuses on developing an ensemble algorithm that enables to denoise the spectral signals while decorrelating the spatio-spectral features concurrently. The developed method is called Wavelet High Dimensional Model (W-HDMR) and combines High Dimensional Model Representation (HDMR) with the Discrete Wavelet Transform (DWT). Through W-HDMR, denoised and decorrelated features are extracted from the HS cubes. HDMR decorrelates each dimension in HS data while DWT denoises the spectral signals. The classification performance of W-HDMR as a new feature extraction technique for HS images is assessed by exploiting a Support Vector Machines algorithm. The results indicate that the proposed W-HDMR method is an efficient feature extraction technique and is considered an adequate tool in the HS classification problem.en_US
dc.identifier.doi10.1080/01431161.2022.2147036
dc.identifier.endpage6920en_US
dc.identifier.issn0143-1161
dc.identifier.issn1366-5901
dc.identifier.issue19-24en_US
dc.identifier.scopus2-s2.0-85146921688en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage6899en_US
dc.identifier.urihttps://doi.org/10.1080/01431161.2022.2147036
dc.identifier.urihttps://hdl.handle.net/20.500.12662/4372
dc.identifier.volume43en_US
dc.identifier.wosWOS:000897079200001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTaylor & Francis Ltden_US
dc.relation.ispartofInternational Journal Of Remote Sensingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHyperspectral dataen_US
dc.subjectfeature extractionen_US
dc.subjectclassificationen_US
dc.subjectwaveletsen_US
dc.subjecthigh dimensional model representationen_US
dc.titleAn efficient feature extraction approach for hyperspectral images using Wavelet High Dimensional Model Representationen_US
dc.typeArticleen_US

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