Joint selection of essential pixels and ...
Type de document :
Article dans une revue scientifique: Article original
PMID :
URL permanente :
Titre :
Joint selection of essential pixels and essential variables across hyperspectral images.
Auteur(s) :
Ghaffari, Mahdiyeh [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement - UMR 8516 [LASIRE]
Omidikia, N. [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]
Omidikia, N. [Auteur]
Ruckebusch, Cyril [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516
Titre de la revue :
Analytica Chimica Acta
Nom court de la revue :
Anal Chim Acta
Numéro :
1141
Pagination :
36-46
Date de publication :
2020-12-08
ISSN :
1873-4324
Mot(s)-clé(s) en anglais :
MCR-ALS
Multivariate curve resolution
Convex hull
Feature selection
Hyperspectral imaging
Multivariate curve resolution
Convex hull
Feature selection
Hyperspectral imaging
Discipline(s) HAL :
Chimie/Chimie théorique et/ou physique
Résumé en anglais : [en]
An approach is proposed and illustrated for the joint selection of essential samples and essential variables of a data matrix in the frame of spectral unmixing. These essential features carry the signals required to linearly ...
Lire la suite >An approach is proposed and illustrated for the joint selection of essential samples and essential variables of a data matrix in the frame of spectral unmixing. These essential features carry the signals required to linearly recover all the information available in the rows and columns of a data set. Working with hyperspectral images, this approach translates into the selection of essential spectral pixels (ESPs) and essential spatial variables (ESVs). This results in a highly-reduced data set, the benefits of which can be minimized computational effort, meticulous data mining, easier model building as well as better problem understanding or interpretation. Working with both simulated and real data, we show that (i) reduction rates of over 99% can be typically obtained, (ii) multivariate curve resolution – alternating least squares (MCR-ALS) can be easily applied on the reduced data sets and (iii) the full distribution maps and spectral profiles can be readily obtained from the reduced profiles and the reduced data sets (without using the full data matrix).Lire moins >
Lire la suite >An approach is proposed and illustrated for the joint selection of essential samples and essential variables of a data matrix in the frame of spectral unmixing. These essential features carry the signals required to linearly recover all the information available in the rows and columns of a data set. Working with hyperspectral images, this approach translates into the selection of essential spectral pixels (ESPs) and essential spatial variables (ESVs). This results in a highly-reduced data set, the benefits of which can be minimized computational effort, meticulous data mining, easier model building as well as better problem understanding or interpretation. Working with both simulated and real data, we show that (i) reduction rates of over 99% can be typically obtained, (ii) multivariate curve resolution – alternating least squares (MCR-ALS) can be easily applied on the reduced data sets and (iii) the full distribution maps and spectral profiles can be readily obtained from the reduced profiles and the reduced data sets (without using the full data matrix).Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Établissement(s) :
Université de Lille
CNRS
CNRS
Collections :
Date de dépôt :
2024-02-28T22:49:03Z
2024-03-12T14:33:21Z
2024-03-12T14:33:21Z