Title :

A new deep learning method for multispectral image time series completion using hyperspectral data

Author(s) :

Cissé, Cheick [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Alboody, Ahed [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Puigt, Matthieu [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Roussel, Gilles [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Vantrepotte, Vincent [Auteur]
Laboratoire d’Océanologie et de Géosciences (LOG) - UMR 8187 [LOG]
Jamet, Cédric [Auteur]
Laboratoire d’Océanologie et de Géosciences (LOG) - UMR 8187 [LOG]
Université du Littoral Côte d'Opale [ULCO]
Tran, Trung-Kien [Auteur]
Laboratoire d’Océanologie et de Géosciences (LOG) - UMR 8187 [LOG]

Conference title :

47th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2022)

Conference organizers(s) :

IEEE

City :

Singapour

Country :

Singapour

Start date of the conference :

2022-05-23

Book title :

Proccedings ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Journal title :

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing

Publisher :

IEEE

Publication date :

2022-04-27

English keyword(s) :

Remote Sensing
Time-Series Completion
Spatio-Temporal Fusion
Deep Learning

HAL domain(s) :

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

English abstract : [en]

The massive development of remote sensing allowed many novel applications which bring new challenges. In particular, some applications such as marine observation require a good spatial, spectral, and temporal resolution. ...
Show more >The massive development of remote sensing allowed many novel applications which bring new challenges. In particular, some applications such as marine observation require a good spatial, spectral, and temporal resolution. In order to tackle the last issue, spatio-temporal fusion of remote sensing data allows to complete a time series of multispectral images from, e.g., hyperspectral images. In this paper, we propose a new deep learning approach to that end. Our main contribution lies in the error completion task which allows to improve the completion performance. We show that our proposed method is able to produce high fidelity predictions with better quality indices than state-of-the-art methods on true images taken from the CIA / LGC database and Sentinel-2 / Sentinel-3 data.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Laboratoire d'Océanologie et de Géosciences (LOG) - UMR 8187

Source :

Harvested from HAL