Title :

MSFA-Net: A convolutional neural network based on multispectral filter arrays for texture feature extraction

Author(s) :

Amziane, Anis [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Losson, Olivier [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Mathon, Benjamin [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Macaire, Ludovic [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]

Journal title :

Pattern Recognition Letters

Pages :

93-99

Publisher :

Elsevier

Publication date :

2023-03-07

ISSN :

0167-8655

English keyword(s) :

Multispectral Imaging
deep learning

HAL domain(s) :

Informatique [cs]/Traitement du signal et de l'image [eess.SP]

English abstract : [en]

Multispectral snapshot cameras fitted with a multispectral filter array (MSFA) acquire several spectral bands in one shot and provide a raw mosaic image in which a single channel value is available at each pixel. Texture ...
Show more >Multispectral snapshot cameras fitted with a multispectral filter array (MSFA) acquire several spectral bands in one shot and provide a raw mosaic image in which a single channel value is available at each pixel. Texture features are classically extracted from fully-defined images that are estimated by demosaicing. Such an estimation may however cause spatio-spectral artifacts. Moreover, texture feature extraction becomes computationally inefficient and yields to high-dimensional features as the number of bands increases. In this paper, we propose an original approach based on a convolutional neural network called MSFA-Net to capture spatio-spectral interactions in raw images at reduced computation costs. Experiments of multispectral image classification and outdoor image segmentation show that the proposed approach outperforms several hand-crafted and deep learning-based feature extractors.Show less >

Language :

Anglais

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL