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Authors: Théo Archambault 1 ; 2 ; Arthur Filoche 1 ; 3 ; Anastase Charantonis 4 ; 5 and Dominique Béréziat 1

Affiliations: 1 Sorbonne Université, CNRS, LIP6, Paris, France ; 2 Sorbonne Université, LOCEAN, Paris, France ; 3 UWA, Perth, Australia ; 4 ENSIIE, LaMME, Evry, France ; 5 Inria, Paris, France

Keyword(s): Image Inverse Problems, Deep Neural Network, Spatiotemporal Inpainting, Multi-Variate Observations, Transfer Learning, Satellite Remote Sensing.

Abstract: The ocean is observed through satellites measuring physical data of various natures. Among them, Sea Surface Height (SSH) and Sea Surface Temperature (SST) are physically linked data involving different remote sensing technologies and therefore different image inverse problems. In this work, we propose to use an Attention-based Encoder-Decoder to perform the inpainting of the SSH, using the SST as contextual information. We propose to pre-train this neural network on a realistic twin experiment of the observing system and to fine-tune it in an unsupervised manner on real-world observations. We show the interest of this strategy by comparing it to existing methods. Our training methodology achieves state-of-the-art performances, and we report a decrease of 25% in error compared to the most widely used interpolations product.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Archambault, T.; Filoche, A.; Charantonis, A. and Béréziat, D. (2024). Pre-Training and Fine-Tuning Attention Based Encoder Decoder Improves Sea Surface Height Multi-Variate Inpainting. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8; ISSN 2184-4321, SciTePress, pages 100-109. DOI: 10.5220/0012357400003660

@conference{visapp24,
author={Théo Archambault. and Arthur Filoche. and Anastase Charantonis. and Dominique Béréziat.},
title={Pre-Training and Fine-Tuning Attention Based Encoder Decoder Improves Sea Surface Height Multi-Variate Inpainting},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={100-109},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012357400003660},
isbn={978-989-758-679-8},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Pre-Training and Fine-Tuning Attention Based Encoder Decoder Improves Sea Surface Height Multi-Variate Inpainting
SN - 978-989-758-679-8
IS - 2184-4321
AU - Archambault, T.
AU - Filoche, A.
AU - Charantonis, A.
AU - Béréziat, D.
PY - 2024
SP - 100
EP - 109
DO - 10.5220/0012357400003660
PB - SciTePress