HyTexiLa: High resolution visible and near infrared hyperspectral texture images

Khan, Haris Ahmad; Mihoubi, Sofiane; Mathon, Benjamin; Thomas, Jean-Baptiste; Hardeberg, Jon Yngve

Document type :

Compte-rendu et recension critique d'ouvrage

DOI :

10.3390/s18072045

Title :

HyTexiLa: High resolution visible and near infrared hyperspectral texture images

Author(s) :

Khan, Haris Ahmad [Auteur]
Laboratoire d'Electronique, d'Informatique et d'Image [EA 7508] [Le2i]
The Norwegian Colour and Visual Computing Laboratory
Mihoubi, Sofiane [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Mathon, Benjamin [Auteur]

Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Thomas, Jean-Baptiste [Auteur]
Laboratoire d'Electronique, d'Informatique et d'Image [EA 7508] [Le2i]
The Norwegian Colour and Visual Computing Laboratory
Hardeberg, Jon Yngve [Auteur]
The Norwegian Colour and Visual Computing Laboratory

Journal title :

Sensors

Pages :

2045

Publisher :

MDPI

Publication date :

2018-06-26

ISSN :

1424-8220

HAL domain(s) :

Informatique [cs]/Traitement des images [eess.IV]

English abstract : [en]

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 ...
Show more >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.Show less >

Language :

Anglais

Popular science :

Non

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

Files

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6068824/pdf
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