Title :

Hyperspectral Video Analysis by Motion and Intensity Preprocessing and Subspace Autoencoding.

Author(s) :

Vitale, Raffaele [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516
Ruckebusch, Cyril [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516
Burud, Ingunn [Auteur]
Norwegian University of Life Sciences [NMBU]
Martens, Harald [Auteur]
Norwegian University of Science and Technology [Trondheim] [NTNU]

Journal title :

Front Chem

Abbreviated title :

Front Chem

Volume number :

10

Pages :

818974

Publication date :

2022-04-05

ISSN :

2296-2646

English keyword(s) :

BIG measurement DATA
on-the-fly processing
extended multiplicative signal correction
light absorption
light scattering
IDLE modelling
motion compensation
hyperspectral videos

HAL domain(s) :

Chimie/Chimie théorique et/ou physique

English abstract : [en]

Hyperspectral imaging has recently gained increasing attention from academic and industrial world due to its capability of providing both spatial and physico-chemical information about the investigated objects. While this ...
Show more >Hyperspectral imaging has recently gained increasing attention from academic and industrial world due to its capability of providing both spatial and physico-chemical information about the investigated objects. While this analytical approach is experiencing a substantial success and diffusion in very disparate scenarios, far less exploited is the possibility of collecting sequences of hyperspectral images over time for monitoring dynamic scenes. This trend is mainly justified by the fact that these so-called hyperspectral videos usually result in BIG DATA sets, requiring TBs of computer memory to be both stored and processed. Clearly, standard chemometric techniques do need to be somehow adapted or expanded to be capable of dealing with such massive amounts of information. In addition, hyperspectral video data are often affected by many different sources of variations in sample chemistry (for example, light absorption effects) and sample physics (light scattering effects) as well as by systematic errors (associated, e.g., to fluctuations in the behaviour of the light source and/or of the camera). Therefore, identifying, disentangling and interpreting all these distinct sources of information represents undoubtedly a challenging task. In view of all these aspects, the present work describes a multivariate hybrid modelling framework for the analysis of hyperspectral videos, which involves spatial, spectral and temporal parametrisations of both known and unknown chemical and physical phenomena underlying complex real-world systems. Such a framework encompasses three different computational steps: 1) motions ongoing within the inspected scene are estimated by optical flow analysis and compensated through IDLE modelling; 2) chemical variations are quantified and separated from physical variations by means of Extended Multiplicative Signal Correction (EMSC); 3) the resulting light scattering and light absorption data are subjected to the On-The-Fly Processing and summarised spectrally, spatially and over time. The developed methodology was here tested on a near-infrared hyperspectral video of a piece of wood undergoing drying. It led to a significant reduction of the size of the original measurements recorded and, at the same time, provided valuable information about systematic variations generated by the phenomena behind the monitored process.Show less >

Language :

Anglais

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

Université de Lille
CNRS

Collections :

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

Research team(s) :

Dynamics, Nanoscopy & Chemometrics (DyNaChem)

Submission date :

2024-02-28T22:30:17Z
2024-03-13T13:07:41Z