CTIS-Net: A Neural Network Architecture for Compressed Learning Based on Computed Tomography Imaging Spectrometers - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue IEEE Transactions on Computational Imaging Année : 2021

CTIS-Net: A Neural Network Architecture for Compressed Learning Based on Computed Tomography Imaging Spectrometers

Résumé

The Computed Tomography Imaging Spectrometer (CTIS) permits a snapshot acquisition of a hyperspectral cube, through the creation of an image of indirect measurements which is then traditionally used for reconstruction of the cube. This reconstruction step is time-consuming and only yields an approximation of the original cube. Following a compressed learning framework, we compare the performance of a classification task carried out on reconstructed cubes on one hand, directly on the raw images on the other. Regarding the latter case, we propose in particular the use of a new Convolutional Neural Network (CNN) architecture called CTIS-Net, whose architecture is tailored to benefit from the specific structure of CTIS images. Results show a sizable increase compared to classification with a standard architecture and compared to a conventional classification on the reconstructed cubes.
Fichier non déposé

Dates et versions

hal-03248567 , version 1 (03-06-2021)

Identifiants

Citer

Clément Douarre, Carlos F Crispim-Junior, Anthony Gelibert, Gérald Germain, Laure Tougne, et al.. CTIS-Net: A Neural Network Architecture for Compressed Learning Based on Computed Tomography Imaging Spectrometers. IEEE Transactions on Computational Imaging, In press, ⟨10.1109/TCI.2021.3083215⟩. ⟨hal-03248567⟩
82 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More