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

Affiliations: 1 LIP6, Sorbonne University, 4 place Jussieu, Paris, France ; 2 LOCEAN, Sorbonne University, 4 place Jussieu, Paris, France ; 3 ENSIIE, Evry, France

Keyword(s): Image Inverse Problems, Deep Neural Network, Spatio Temporal Interpolation, Multimodal Observations, Unsupervised Neural Network, Satellite Remote Sensing.

Abstract: Satellite remote sensing is a key technique to understand Ocean dynamics. Due to measurement difficulties, various ill-posed image inverse problems occur, and among them, gridding satellite Ocean altimetry maps is a challenging interpolation of sparse along-tracks data. In this work, we show that it is possible to take advantage of better-resolved physical data to enhance Sea Surface Height (SSH) gridding using only partial data acquired via satellites. For instance, the Sea Surface Temperature (SST) is easier to measure through satellite and has an underlying physical link with altimetry. We train a deep neural network to estimate a time series of SSH using a time series of SST in an unsupervised way. We compare to state-of-the-art methods and report a 13% RMSE decrease compared to the operational altimetry algorithm.

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Paper citation in several formats:
Archambault, T.; Filoche, A.; Charantonis, A. and Béréziat, D. (2023). Multimodal Unsupervised Spatio-Temporal Interpolation of Satellite Ocean Altimetry Maps. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7; ISSN 2184-4321, SciTePress, pages 159-167. DOI: 10.5220/0011620100003417

@conference{visapp23,
author={Théo Archambault. and Arthur Filoche. and Anastase Charantonis. and Dominique Béréziat.},
title={Multimodal Unsupervised Spatio-Temporal Interpolation of Satellite Ocean Altimetry Maps},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},
year={2023},
pages={159-167},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011620100003417},
isbn={978-989-758-634-7},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP
TI - Multimodal Unsupervised Spatio-Temporal Interpolation of Satellite Ocean Altimetry Maps
SN - 978-989-758-634-7
IS - 2184-4321
AU - Archambault, T.
AU - Filoche, A.
AU - Charantonis, A.
AU - Béréziat, D.
PY - 2023
SP - 159
EP - 167
DO - 10.5220/0011620100003417
PB - SciTePress