%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture non répertoriées par l'AERES
%A Dorffer, C.
%A Jourdin, F.
%A Nguyen, T.T.N.
%A Devillers, Rodolphe
%A Mouillot, D.
%A Fablet, R.
%T Observation-only deep learning for gappy satellite-derived ocean color data using 4DVarNet
%D 2025
%L fdi:010095766
%G ENG
%J IEEE Transactions on Geoscience and Remote Sensing
%@ 0196-2892
%K MEDITERRANEE
%M  ISI:001626459000022
%P 4212512 [12 ]
%R 10.1109/tgrs.2025.3624465
%U https://www.documentation.ird.fr/hor/fdi:010095766
%> https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2025-12/010095766.pdf
%V 63
%W Horizon (IRD)
%X Monitoring optical properties of coastal and open ocean waters is crucial to assessing the health of marine ecosystems. Deep learning offers a promising approach to address these ecosystem dynamics, especially in scenarios where gapfree ground-truth data is lacking, which poses a challenge for designing effective training frameworks. Using an advanced neural variational data assimilation scheme (called 4DVarNet), we introduce a comprehensive training framework designed to effectively train directly on gappy data sets. Using the Mediterranean Sea as a case study, our experiments not only highlight the high performance of the chosen neural network in reconstructing gap-free images from gappy datasets but also demonstrate its superior performance over state-of-the-art algorithms such as DInEOF and end-to-end neural mapping schemes based CNN or UNet architectures.
%$ 030 ; 122 ; 126 ; 020