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dc.contributor.authorDeborah, Hilda
dc.date.accessioned2023-02-09T11:24:54Z
dc.date.available2023-02-09T11:24:54Z
dc.date.created2022-11-28T10:50:55Z
dc.date.issued2022
dc.identifier.issn2158-6276
dc.identifier.urihttps://hdl.handle.net/11250/3049627
dc.description.abstractHyperspectral imaging has the potential of delivering highly accurate results due to its high spatial and spectral resolutions. However, to ensure relevant and highly accurate end results, the processing steps need to go through rigorous quality assessments. This article provides a generic hyperspectral dataset suitable for designing quality assessment protocols for spectral image processing algorithms. The dataset consists of hyperspectral images of 195 pigment patches and spectral libraries originating from 327 unique pigments. Additionally, two examples of how it can be used for the evaluation of distance functions are also provided.en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.titleHyperspectral Pigment Dataseten_US
dc.title.alternativeHyperspectral Pigment Dataseten_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionacceptedVersionen_US
dc.rights.holder© IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.source.journalWorkshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensingen_US
dc.identifier.doi10.1109/WHISPERS56178.2022.9955067
dc.identifier.cristin2082251
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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