On-the-fly spectral unmixing based on Kalman filtering

Kouakou, H.; Goulart, J. H. D.; Vitale, Raffaele; Oberlin, T.; Rousseau, D.; Ruckebusch, Cyril; Dobigeon, N.

Type de document :

Article dans une revue scientifique: Article original

DOI :

10.1016/j.chemolab.2024.105252

URL permanente :

http://hdl.handle.net/20.500.12210/119120

Titre :

On-the-fly spectral unmixing based on Kalman filtering

Auteur(s) :

Kouakou, H. [Auteur]
Université de Toulouse [UT]
Goulart, J. H. D. [Auteur]
Université de Toulouse [UT]
Vitale, Raffaele [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516
Oberlin, T. [Auteur]
Université de Toulouse [UT]
Rousseau, D. [Auteur]
Université d'Angers [UA]
Ruckebusch, Cyril [Auteur]

Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516
Dobigeon, N. [Auteur]
Université de Toulouse [UT]

Titre de la revue :

Chemometrics Intell. Lab. Syst.

Nom court de la revue :

Chemometrics Intell. Lab. Syst.

Numéro :

255

Pagination :

Date de publication :

2024-12-07

ISSN :

0169-7439

Mot(s)-clé(s) en anglais :

Spectral unmixing
On-the-fly processing
Kalman filter
Essential spectral information

Discipline(s) HAL :

Chimie/Chimie théorique et/ou physique

Résumé en anglais : [en]

This work introduces an on-the-fly (i.e., online) linear spectral unmixing method which is able to sequentially analyze spectral data acquired on a spectrum-by-spectrum basis. After deriving a sequential counterpart of the ...
Lire la suite >This work introduces an on-the-fly (i.e., online) linear spectral unmixing method which is able to sequentially analyze spectral data acquired on a spectrum-by-spectrum basis. After deriving a sequential counterpart of the conventional linear mixing model, the proposed approach recasts the linear unmixing problem into a linear state-space estimation framework. Under Gaussian noise and state models, the estimation of the pure spectra can be efficiently conducted by resorting to Kalman filtering. Interestingly, it is shown that this Kalman filter can operate in a lower-dimensional subspace to lighten the computational burden of the overall unmixing procedure. Experimental results obtained on synthetic and real Raman data sets show that this Kalman filter-based method offers a convenient trade-off between unmixing accuracy and computational efficiency, which is crucial for operating in an on-the-fly setting. The proposed method constitutes a valuable building block for benefiting from acquisition and processing frameworks recently proposed in the microscopy literature, which are motivated by practical issues such as reducing acquisition time and avoiding potential damages being inflicted to photosensitive samples. The code associated with the numerical illustrations reported in this paper is freely available online at https://github.com/HKouakou/KF-OSULire moins >

Langue :

Anglais

Audience :

Internationale

Vulgarisation :

Non

Établissement(s) :

Université de Lille
CNRS

Collections :

Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516

Équipe(s) de recherche :

Dynamics, Nanoscopy & Chemometrics (DyNaChem)

Date de dépôt :

2024-12-09T22:02:26Z
2024-12-18T08:13:59Z

Fichiers

1-s2.0-S0169743924001928-main.pdf
Version éditeur
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Ce document est diffusé sous licence Attribution-NonCommercial 3.0 United States

Numéro de version	Lien	Date de modification
2	20.500.12210/119120*	2024-12-18T08:10:01Z
1	20.500.12210/119120.1	2024-12-09T22:02:26Z

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