{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T17:56:39Z","timestamp":1767894999789,"version":"3.49.0"},"reference-count":82,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2018,6,26]],"date-time":"2018-06-26T00:00:00Z","timestamp":1529971200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s18072045","type":"journal-article","created":{"date-parts":[[2018,6,27]],"date-time":"2018-06-27T11:02:05Z","timestamp":1530097325000},"page":"2045","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["HyTexiLa: High Resolution Visible and Near Infrared Hyperspectral Texture Images"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4305-1935","authenticated-orcid":false,"given":"Haris","family":"Khan","sequence":"first","affiliation":[{"name":"The Norwegian Colour and Visual Computing Laboratory, NTNU\u2013Norwegian University of Science and Technology, 2815 Gj\u00f8vik, Norway"},{"name":"Le2i, FRE CNRS 2005, Universit\u00e9 Bourgogne Franche-Comt\u00e9, 21000 Dijon, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5832-4938","authenticated-orcid":false,"given":"Sofiane","family":"Mihoubi","sequence":"additional","affiliation":[{"name":"Univ. Lille, CNRS, Centrale Lille, UMR 9189\u2014CRIStAL, Centre de Recherche en Informatique Signal et Automatique de Lille, F-59000 Lille, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9002-6711","authenticated-orcid":false,"given":"Benjamin","family":"Mathon","sequence":"additional","affiliation":[{"name":"Univ. Lille, CNRS, Centrale Lille, UMR 9189\u2014CRIStAL, Centre de Recherche en Informatique Signal et Automatique de Lille, F-59000 Lille, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jean-Baptiste","family":"Thomas","sequence":"additional","affiliation":[{"name":"The Norwegian Colour and Visual Computing Laboratory, NTNU\u2013Norwegian University of Science and Technology, 2815 Gj\u00f8vik, Norway"},{"name":"Le2i, FRE CNRS 2005, Universit\u00e9 Bourgogne Franche-Comt\u00e9, 21000 Dijon, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jon","family":"Hardeberg","sequence":"additional","affiliation":[{"name":"The Norwegian Colour and Visual Computing Laboratory, NTNU\u2013Norwegian University of Science and Technology, 2815 Gj\u00f8vik, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,6,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wolfe, W.L. 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