Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Quentin Febvre"'
Autor:
Zhanwen Gao, Bertrand Chapron, Chunyong Ma, Ronan Fablet, Quentin Febvre, Wenxia Zhao, Ge Chen
Publikováno v:
Geophysical Research Letters, Vol 51, Iss 7, Pp n/a-n/a (2024)
Abstract Extracting balanced geostrophic motions (BM) from sea surface height (SSH) observations obtained by wide‐swath altimetry holds great significance in enhancing our understanding of oceanic dynamic processes at submesoscale wavelength. Howev
Externí odkaz:
https://doaj.org/article/93248c72a7424db2a037ddd6128b7e10
Publikováno v:
EURASIP Journal on Image and Video Processing, Vol 2023, Iss 1, Pp 1-30 (2023)
Abstract Training Convolutional Neural Networks (CNN) is a resource-intensive task that requires specialized hardware for efficient computation. One of the most limiting bottlenecks of CNN training is the memory cost associated with storing the activ
Externí odkaz:
https://doaj.org/article/c22705eb6df54be494103044d0fa92b6
Publikováno v:
Environmental Data Science, Vol 2 (2023)
Externí odkaz:
https://doaj.org/article/546372399a014806b520dedb814a6b87
In oceanography, altimetry products are used to measure the height of the ocean surface, and ocean modeling is used to understand and predict the behavior of the ocean. There are two main types of gridded altimetry products: operational sea level pro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7d89ef6c3b5c3c42ddb616543bcda9a4
https://doi.org/10.5194/egusphere-egu23-8288
https://doi.org/10.5194/egusphere-egu23-8288
Publikováno v:
Ieee Transactions On Geoscience And Remote Sensing (0196-2892) (Institute of Electrical and Electronics Engineers (IEEE)), 2023, Vol. 61, P. 4204214 (14p.)
Due to the irregular space-time sampling of sea surface observations, the reconstruction of sea surface dynamics is a challenging inverse problem. While satellite altimetry provides a direct observation of the sea surface height (SSH), which relates
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b900eab97b80278662d30b60018e06d5
https://archimer.ifremer.fr/doc/00835/94680/
https://archimer.ifremer.fr/doc/00835/94680/
Publikováno v:
Geoscientific Model Development
Geoscientific Model Development, 2023, 16 (8), pp.2119--2147. ⟨10.5194/gmd-16-1-2023⟩
Geoscientific Model Development, 2023, 16 (8), pp.2119--2147. ⟨10.5194/gmd-16-1-2023⟩
The reconstruction of sea surface currents from satellite altimeter data is a key challenge in spatial oceanography, especially with the upcoming wide-swath SWOT (Surface Water and Ocean and Topography) altimeter mission. Operational systems, however
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::097c847017faf7ab7a71f17d042601dd
https://gmd.copernicus.org/preprints/gmd-2022-241/
https://gmd.copernicus.org/preprints/gmd-2022-241/
Satellite radar altimeters are a key source of observation of ocean surface dynamics. However, current sensor technology and mapping techniques do not yet allow to systematically resolve scales smaller than 100km. With their new sensors, upcoming wid
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::62f0ca3600a111b0ba3375148cc85efe
https://doi.org/10.5194/egusphere-egu22-12549
https://doi.org/10.5194/egusphere-egu22-12549
Publikováno v:
IGARSS
The reconstruction of better-resolved sea surface currents is a key challenge in space oceanography. Besides the upcoming SWOT wide-swath altimeter mission, new algorithms are explore to produce improved gap-free gridded products. Based on the recent
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bdfaab9431c589b9ab1febf8d2bd91a1
https://doi.org/10.1109/igarss47720.2021.9554800
https://doi.org/10.1109/igarss47720.2021.9554800
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (2194-9050) (Copernicus GmbH), 2021-06-17, Vol. V-3-2021, P. 295-302
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol V-3-2021, Pp 295-302 (2021)
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2021, V-3-2021, pp.295-302. ⟨10.5194/isprs-annals-V-3-2021-295-2021⟩
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol V-3-2021, Pp 295-302 (2021)
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2021, V-3-2021, pp.295-302. ⟨10.5194/isprs-annals-V-3-2021-295-2021⟩
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fffa27bbea3c3ba881a0d550cf247fad
https://archimer.ifremer.fr/doc/00806/91770/
https://archimer.ifremer.fr/doc/00806/91770/
Publikováno v:
ICCV Workshops
Convolutional Neural Networks (CNN) have demonstrated state-of-the-art results on various computer vision problems. However, training CNNs require specialized GPU with large memory. GPU memory has been a major bottleneck of the CNN training procedure
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::998dc2b65eb4d3e9d99cafd85ce98f7d
https://doi.org/10.1109/iccvw.2019.00258
https://doi.org/10.1109/iccvw.2019.00258