FN Archimer Export Format
PT J
TI End-to-End Physics-Informed Representation Learning from and for Satellite Ocean Remote Sensing Data : Applications to Satellite Altimetry and sea Surface Currents
BT 
AF Fablet, Ronan
   Amar, Mohamed Mahmoud
   Febvre, Quentin
   Beauchamp, Maxime
   Chapron, Bertrand
AS 1:1;2:1;3:1;4:1;5:2;
FF 1:;2:;3:;4:;5:PDG-ODE-LOPS-SIAM;
C1 IMT Atlantique, UMR CNRS Lab-STICC, Brest, France.
   Ifremer, UMR CNRS LOPS, Brest, France
C2 IMT ATLANTIQUE, FRANCE
   IFREMER, FRANCE
SI BREST
SE PDG-ODE-LOPS-SIAM
TC 0
UR https://archimer.ifremer.fr/doc/00806/91770/97753.pdf
LA English
DT Article
DE ;Space oceanography;sea surface dynamics;satellite altimetry;SWOT mission;end-to-end learning;inverse problems;data assimilation;space-time interpolation;short-term forecasting;adaptive sampling.
AB This paper addresses physics-informed deep learning schemes for satellite ocean remote sensing data. Such observation datasets are characterized by the irregular space-time sampling of the ocean surface due to sensors’ characteristics and satellite orbits. With a focus on satellite altimetry, we show that end-to-end learning schemes based on variational formulations provide new means to explore and exploit such observation datasets. Through Observing System Simulation Experiments (OSSE) using numerical ocean simulations and real nadir and wide-swath altimeter sampling patterns, we demonstrate their relevance w.r.t. state-of-the-art and operational methods for space-time interpolation and short-term forecasting issues. We also stress and discuss how they could contribute to the design and calibration of ocean observing systems. 
PY 2021
PD JUL
SO ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
SN 2194-9050
PU Copernicus GmbH
VL V-3-2021
BP 295
EP 302
DI 10.5194/isprs-annals-V-3-2021-295-2021
ID 91770
ER

EF