dc.contributor.author | Khan, Haris Ahmad | |
dc.contributor.author | Mihoubi, Soufiane | |
dc.contributor.author | Mathon, Benjamin | |
dc.contributor.author | Thomas, Jean-Baptiste | |
dc.contributor.author | Hardeberg, Jon Yngve | |
dc.date.accessioned | 2019-02-22T10:00:05Z | |
dc.date.available | 2019-02-22T10:00:05Z | |
dc.date.created | 2018-08-14T13:17:27Z | |
dc.date.issued | 2018 | |
dc.identifier.issn | 1424-8220 | |
dc.identifier.uri | http://hdl.handle.net/11250/2586980 | |
dc.description.abstract | We present a dataset of close range hyperspectral images of materials that span the visible and near infrared spectrums: HyTexiLa (Hyperspectral Texture images acquired in Laboratory). The data is intended to provide high spectral and spatial resolution reflectance images of 112 materials to study spatial and spectral textures. In this paper we discuss the calibration of the data and the method for addressing the distortions during image acquisition. We provide a spectral analysis based on non-negative matrix factorization to quantify the spectral complexity of the samples and extend local binary pattern operators to the hyperspectral texture analysis. The results demonstrate that although the spectral complexity of each of the textures is generally low, increasing the number of bands permits better texture classification, with the opponent band local binary pattern feature giving the best performance. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | MDPI | nb_NO |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | HyTexiLa: high resolution visible and near infrared hyperspectral texture images | nb_NO |
dc.type | Journal article | nb_NO |
dc.type | Peer reviewed | nb_NO |
dc.description.version | publishedVersion | nb_NO |
dc.source.volume | 18 | nb_NO |
dc.source.journal | Sensors | nb_NO |
dc.source.issue | 7 | nb_NO |
dc.identifier.doi | 10.3390/s18072045 | |
dc.identifier.cristin | 1601927 | |
dc.relation.project | Norges forskningsråd: 536305 | nb_NO |
dc.description.localcode | (C) 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | nb_NO |
cristin.unitcode | 194,0,0,0 | |
cristin.unitcode | 194,63,10,0 | |
cristin.unitname | Norges teknisk-naturvitenskapelige universitet | |
cristin.unitname | Institutt for datateknologi og informatikk | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |