Exploring hyperspectral imaging data sets ...
Type de document :
Article dans une revue scientifique: Article original
URL permanente :
Titre :
Exploring hyperspectral imaging data sets with topological data analysis
Auteur(s) :
Duponchel, Ludovic [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 (LASIRE) - UMR 8516
Titre de la revue :
Analytica Chimica Acta
Numéro :
1000
Pagination :
123-131
Date de publication :
2018-02
Discipline(s) HAL :
Chimie/Chimie théorique et/ou physique
Résumé en anglais : [en]
Analytical chemistry is rapidly changing. Indeed we acquire always more data in order to go ever further in the exploration of complex samples. Hyperspectral imaging has not escaped this trend. It quickly became a tool of ...
Lire la suite >Analytical chemistry is rapidly changing. Indeed we acquire always more data in order to go ever further in the exploration of complex samples. Hyperspectral imaging has not escaped this trend. It quickly became a tool of choice for molecular characterisation of complex samples in many scientific domains. The main reason is that it simultaneously provides spectral and spatial information. As a result, chemometrics has provided many exploration tools (PCA, clustering, MCR-ALS …) well-suited for such data structure at early stage. However we are today facing a new challenge considering the always increasing number of pixels in the data cubes we have to manage. The idea is therefore to introduce a new paradigm of Topological Data Analysis in order explore hyperspectral imaging data sets highlighting its nice properties and specific features. With this paper, we shall also point out the fact that conventional chemometric methods are often based on variance analysis or simply impose a data model which implicitly defines the geometry of the data set. Thus we will show that it is not always appropriate in the framework of hyperspectral imaging data sets exploration.Lire moins >
Lire la suite >Analytical chemistry is rapidly changing. Indeed we acquire always more data in order to go ever further in the exploration of complex samples. Hyperspectral imaging has not escaped this trend. It quickly became a tool of choice for molecular characterisation of complex samples in many scientific domains. The main reason is that it simultaneously provides spectral and spatial information. As a result, chemometrics has provided many exploration tools (PCA, clustering, MCR-ALS …) well-suited for such data structure at early stage. However we are today facing a new challenge considering the always increasing number of pixels in the data cubes we have to manage. The idea is therefore to introduce a new paradigm of Topological Data Analysis in order explore hyperspectral imaging data sets highlighting its nice properties and specific features. With this paper, we shall also point out the fact that conventional chemometric methods are often based on variance analysis or simply impose a data model which implicitly defines the geometry of the data set. Thus we will show that it is not always appropriate in the framework of hyperspectral imaging data sets exploration.Lire moins >
Langue :
Anglais
Audience :
Internationale
Vulgarisation :
Non
Établissement(s) :
CNRS
ENSCL
Université de Lille
ENSCL
Université de Lille
Collections :
Équipe(s) de recherche :
Propriétés magnéto structurales des matériaux (PMSM)
Date de dépôt :
2021-11-16T08:23:32Z
2024-02-21T07:46:29Z
2024-02-21T07:46:29Z