Hyperspectral image denoising with enhanced multivariance product representation

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dc.contributor.authorOzay, Evrim Korkmaz
dc.contributor.authorTunga, Burcu
dc.date.accessioned2024-03-13T10:30:51Z
dc.date.available2024-03-13T10:30:51Z
dc.date.issued2022
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description.abstractHyperspectral images are used in many different fields due to their ability to capture wide areas and rich spectrality. However, applications on hyperspectral image (HSI) are affected or limited by various types of noise. Therefore, denoising is an important pre-processing technique for HSI analysis. Tensor decomposition-based denoising algorithms are frequently used due to constraints of traditional two-dimensional methods. An alternative tensor decomposition, enhanced multivariance product representation (EMPR) has been derived from high-dimensional model representation (HDMR) for multivariate functions and discretized for tensor-type data sets. In this study, EMPR-based denoising method is proposed for HSI denoising. EMPR is a decomposition method which is easy to compute and does not include a rank problem that exists in the other tensor decomposition methods. The performance of EMPR-based denoising is evaluated by means of simulated and real experiments on different HSI data sets which include different types of noise. The obtained results are compared with the state-of-the-art tensor-based methods.en_US
dc.identifier.doi10.1007/s11760-021-02062-6
dc.identifier.endpage1133en_US
dc.identifier.issn1863-1703
dc.identifier.issn1863-1711
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85122686580en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1127en_US
dc.identifier.urihttps://doi.org/10.1007/s11760-021-02062-6
dc.identifier.urihttps://hdl.handle.net/20.500.12662/3562
dc.identifier.volume16en_US
dc.identifier.wosWOS:000740415700001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofSignal Image And Video Processingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHyperspectral imageen_US
dc.subjectDenoisingen_US
dc.subjectTensor decompositionen_US
dc.subjectEnhanced multivariance product representationen_US
dc.titleHyperspectral image denoising with enhanced multivariance product representationen_US
dc.typeArticleen_US

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