| dc.contributor.author | Deborah, Hilda | |
| dc.date.accessioned | 2023-02-09T11:24:54Z | |
| dc.date.available | 2023-02-09T11:24:54Z | |
| dc.date.created | 2022-11-28T10:50:55Z | |
| dc.date.issued | 2022 | |
| dc.identifier.issn | 2158-6276 | |
| dc.identifier.uri | https://hdl.handle.net/11250/3049627 | |
| dc.description.abstract | Hyperspectral 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.iso | eng | en_US |
| dc.publisher | IEEE | en_US |
| dc.title | Hyperspectral Pigment Dataset | en_US |
| dc.title.alternative | Hyperspectral Pigment Dataset | en_US |
| dc.type | Peer reviewed | en_US |
| dc.type | Journal article | en_US |
| dc.description.version | acceptedVersion | en_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.journal | Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing | en_US |
| dc.identifier.doi | 10.1109/WHISPERS56178.2022.9955067 | |
| dc.identifier.cristin | 2082251 | |
| cristin.ispublished | true | |
| cristin.fulltext | postprint | |
| cristin.qualitycode | 1 | |
