FN Archimer Export Format
PT C
TI Guided Deep Learning by Subaperture Decomposition: Ocean Patterns from SAR Imagery
BT 
AF Ristea, Nicolae-Catalin
   Anghel, Andrei
   Datcu, Mihai
   Chapron, Bertrand
AS 1:1;2:1;3:1,2;4:3;
FF 1:;2:;3:;4:PDG-ODE-LOPS-SIAM;
C1 CEOSpaceTech, University Politehnica of Bucharest, Romania
   Remote Sensing Technology Institute, German Aerospace Center (DLR), Germany
   Laboratoire d’Ocanographie Physique et Spatiale (LOPS), Ifremer, Brest, France
C2 UNIV POLYTEHNICA BUCAREST, ROMANIA
   DLR, GERMANY
   IFREMER, FRANCE
SI BREST
SE PDG-ODE-LOPS-SIAM
UM LOPS
IN WOS Ifremer UMR
   copubli-europe
UR https://archimer.ifremer.fr/doc/00801/91314/97105.pdf
LA English
DT Proceedings paper
DE ;Subapertures decomposition;remote sensing;SAR;deep learning;unsupervised segmentation
AB Spaceborne synthetic aperture radar (SAR) can provide meters-scale images of the ocean surface roughness day-or-night in nearly all weather conditions. This makes it a unique asset for many geophysical applications. Sentinel-l SAR wave mode (WV) vignettes have made possible to capture many important oceanic and atmospheric phenomena since 2014. However, considering the amount of data provided, expanding applications requires a strategy to automatically process and extract geophysical parameters. In this study, we propose to apply subaperture decomposition (SD) as a preprocessing stage for SAR deep learning models. Our data-centring approach surpassed the baseline by 0.7%, obtaining state-of-the-art on the TenGeoP-SARwv data set. In addition, we empirically showed that SD could bring additional information over the original vignette, by rising the number of clusters for an unsupervised segmentation method. Overall, we encourage the development of data-centring approaches, showing that, data preprocessing could bring significant performance improvements over existing deep learning models. 
PY 2022
PD JUN
CT IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 2022, pp. 6825-6828
DI 10.1109/IGARSS46834.2022.9884291
ID 91314
ER

EF