Edge-Preserving Image Smoothing Constraint ...
Type de document :
Article dans une revue scientifique: Article original
DOI :
URL permanente :
Titre :
Edge-Preserving Image Smoothing Constraint in Multivariate Curve Resolution–Alternating Least Squares (MCR-ALS) of Hyperspectral Data
Auteur(s) :
Hugelier, Siewert [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement - UMR 8516 [LASIRE]
Vitale, Raffaele [Auteur]
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]
Vitale, Raffaele [Auteur]
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
Titre de la revue :
Applied Spectroscopy
Numéro :
72
Pagination :
420-431
Date de publication :
2018-03
Mot(s)-clé(s) en anglais :
Hyperspectral image analysis
MCR-ALS
constraint
edge preservation
multivariate curve resolution–alternating least squares
spatial smoothing
MCR-ALS
constraint
edge preservation
multivariate curve resolution–alternating least squares
spatial smoothing
Discipline(s) HAL :
Chimie/Chimie théorique et/ou physique
Résumé en anglais : [en]
This article explores smoothing with edge-preserving properties as a spatial constraint for the resolution of hyperspectral images with multivariate curve resolution-alternating least squares (MCR-ALS). For each constrained ...
Lire la suite >This article explores smoothing with edge-preserving properties as a spatial constraint for the resolution of hyperspectral images with multivariate curve resolution-alternating least squares (MCR-ALS). For each constrained component image (distribution map), irrelevant spatial details and noise are smoothed applying an L1- or L0-norm penalized least squares regression, highlighting in this way big changes in intensity of adjacent pixels. The feasibility of the constraint is demonstrated on three different case studies, in which the objects under investigation are spatially clearly defined, but have significant spectral overlap. This spectral overlap is detrimental for obtaining a good resolution and additional spatial information should be provided. The final results show that the spatial constraint enables better image (map) abstraction, artifact removal, and better interpretation of the results obtained, compared to a classical MCR-ALS analysis of hyperspectral images.Lire moins >
Lire la suite >This article explores smoothing with edge-preserving properties as a spatial constraint for the resolution of hyperspectral images with multivariate curve resolution-alternating least squares (MCR-ALS). For each constrained component image (distribution map), irrelevant spatial details and noise are smoothed applying an L1- or L0-norm penalized least squares regression, highlighting in this way big changes in intensity of adjacent pixels. The feasibility of the constraint is demonstrated on three different case studies, in which the objects under investigation are spatially clearly defined, but have significant spectral overlap. This spectral overlap is detrimental for obtaining a good resolution and additional spatial information should be provided. The final results show that the spatial constraint enables better image (map) abstraction, artifact removal, and better interpretation of the results obtained, compared to a classical MCR-ALS analysis of hyperspectral images.Lire moins >
Langue :
Anglais
Audience :
Internationale
Vulgarisation :
Non
Établissement(s) :
CNRS
ENSCL
Université de Lille
ENSCL
Université de Lille
Collections :
Équipe(s) de recherche :
Dynamics, Nanoscopy & Chemometrics (DyNaChem)
Date de dépôt :
2021-11-16T08:23:33Z
2024-02-14T12:55:01Z
2024-02-14T12:55:01Z