Model-based co-clustering for hyperspectral images
Document type :
Article dans une revue scientifique
DOI :
Permalink :
Title :
Model-based co-clustering for hyperspectral images
Author(s) :
Jacques, Julien [Auteur]
Ruckebusch, Cyril [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement - UMR 8516 [LASIRE]
Ruckebusch, Cyril [Auteur]

Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement - UMR 8516 [LASIRE]
Journal title :
Journal of Spectral Imaging
Abbreviated title :
JSI
Volume number :
5
Pages :
a3-1-a3-6
Publisher :
IM Publications
Publication date :
2016
Keyword(s) :
Co-clustering
Hyperspectral images
Latent block model
Hyperspectral images
Latent block model
HAL domain(s) :
Mathématiques [math]/Statistiques [math.ST]
English abstract : [en]
A model-based co-clustering algorithm for hyperspectral images is presented. This algorithm, which relies on the probabilistic latent block model for continuous data, aims to cluster both the pixels and the spectral features ...
Show more >A model-based co-clustering algorithm for hyperspectral images is presented. This algorithm, which relies on the probabilistic latent block model for continuous data, aims to cluster both the pixels and the spectral features of the images. This approach has been applied to a benchmark Raman imaging dataset and revealed relevant information for spatial-spectral exploratory investigation of the data.Show less >
Show more >A model-based co-clustering algorithm for hyperspectral images is presented. This algorithm, which relies on the probabilistic latent block model for continuous data, aims to cluster both the pixels and the spectral features of the images. This approach has been applied to a benchmark Raman imaging dataset and revealed relevant information for spatial-spectral exploratory investigation of the data.Show less >
Language :
Anglais
Audience :
Internationale
Popular science :
Non
Administrative institution(s) :
CNRS
ENSCL
Université de Lille
ENSCL
Université de Lille
Submission date :
2020-06-08T14:10:22Z
2020-06-09T08:28:21Z
2020-06-09T08:28:21Z
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