Supervision : Dominique BÉRÉZIAT

Downscaling of oceans fields by fusion of heterogeneous observations using Deep Learning Algorithms

Satellite images of heterogeneous data (SST, SSH, salinity, biological activity) are taken at different scales. This subject focuses on using the highest resolution images (SST) to enhance the images of coarse resolution (SSH). Another objective is to estimate the ocean current fields. We want to use Deep Learning algorithms on these super resolution key questions.

2022-2023 Publications

• 2023
  • S. Thiria, Ch. Sorror, Th. Archambault, A. Charantonis, D. Béréziat, C. Mejia, J.‑M. Molines, M. Crépon : “Downscaling of ocean fields by fusion of heterogeneous observations using Deep Learning algorithms”, Ocean Modelling, vol. 182, pp. 102174, (Elsevier) (2023)
  • Th. Archambault, A. Filoche, A. Charantonnis, D. Béréziat : “Multimodal Unsupervised Spatio-Temporal Interpolation of satellite ocean altimetry maps”, Proceedings of the 18^th International Conference on Computer Vision Theory and Applications, Lisboa, Portugal (2023)
• 2022
  • A. Filoche, Th. Archambault, A. Charantonis, D. Béréziat : “Statistics-free interpolation of ocean observations with deep spatio-temporal prior”, ECML/PKDD Workshop on Machine Learning for Earth Observation and Prediction (MACLEAN), Grenoble, France (2022)
  • Th. Archambault, A. Charantonis, D. Béréziat, C. Mejia, S. Thiria : “SSH Super-Resolution using high resolution SST with a Subpixel Convolutional Residual Network”, Climate Informatics, Asheville, NC, United States (2022)
  • Th. Archambault, A. Filoche, A. Charantonis, D. Béréziat : “Unlearned Downscaling of sea surface height with Deep Image Prior”, IA for Earth Sciences Workshop The International Conference on Learning Representations (ICLR), Virtual conference, United States (2022)