Title :

Exploring local spatial features in hyperspectral images

Author(s) :

Ahmad, Mohamad [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]
Silva, C. S. [Auteur]
Ruckebusch, Cyril [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516
Cocchi, M. [Auteur]

Journal title :

Journal of Chemometrics

Abbreviated title :

J. Chemometr.

Volume number :

-

Publication date :

2020-09-05

ISSN :

0886-9383

English keyword(s) :

gray-level co-occurrence matrix
hyperspectral images
multivariate image analysis
spatial features
wavelet transform

HAL domain(s) :

Chimie

English abstract : [en]

We propose a methodological framework to extract spatial features in hyperspectral imaging data and establish a link between these features and the spectral regions, capturing the observed structural patterns. The proposed ...
Show more >We propose a methodological framework to extract spatial features in hyperspectral imaging data and establish a link between these features and the spectral regions, capturing the observed structural patterns. The proposed approach consists of five main steps: (i) two-dimensional stationary wavelet transform (2D-SWT) is applied to a hyperspectral data cube, decomposing each single-channel image with a selected wavelet filter up to the maximum decomposition level; (ii) a gray-level co-occurrence matrix is calculated for every 2D-SWT image resulting from stage (i); (iii) distinctive spatial features are determined by computing morphological descriptors from each gray-level co-occurrence matrix; (iv) the morphological descriptors are rearranged in a two-dimensional data array; and (v) this data matrix is subjected to principal component analysis (PCA) for exploring the variability of the aforementioned descriptors across spectral channels. As a result, groups of spectral wavelengths associated to specific spatial features can be pointed out yielding a better understanding and interpretation of the data. In principle, this information can also be further exploited, for example, to improve the separation of pure spectral profiles in a multivariate curve resolution context.Show less >

Language :

Anglais

Peer reviewed article :

Oui

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

Submission date :

2024-02-28T23:22:41Z
2024-03-12T13:26:18Z