A Combination of Feature Tracking and Pattern Matching with Optimal Parametrization for Sea Ice Drift Retrieval from SAR Data
International audience Sea ice drift strongly influences sea ice thickness distribution and indirectly controls air-sea ice-ocean interactions. Estimating sea ice drift over a large range of spatial and temporal scales is therefore needed to characterize the properties of sea ice dynamics and to bet...
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ftccsdartic:oai:HAL:hal-03405346v1 2023-05-15T18:16:13+02:00 A Combination of Feature Tracking and Pattern Matching with Optimal Parametrization for Sea Ice Drift Retrieval from SAR Data Korosov, Anton, Rampal, Pierre Nansen Environmental and Remote Sensing Center Bergen (NERSC) 2017-03-11 https://hal.univ-grenoble-alpes.fr/hal-03405346 https://hal.univ-grenoble-alpes.fr/hal-03405346/document https://hal.univ-grenoble-alpes.fr/hal-03405346/file/Korosov2017aRemote_Sensing.pdf https://doi.org/10.3390/rs9030258 en eng HAL CCSD MDPI info:eu-repo/semantics/altIdentifier/doi/10.3390/rs9030258 hal-03405346 https://hal.univ-grenoble-alpes.fr/hal-03405346 https://hal.univ-grenoble-alpes.fr/hal-03405346/document https://hal.univ-grenoble-alpes.fr/hal-03405346/file/Korosov2017aRemote_Sensing.pdf doi:10.3390/rs9030258 info:eu-repo/semantics/OpenAccess ISSN: 2072-4292 Remote Sensing https://hal.univ-grenoble-alpes.fr/hal-03405346 Remote Sensing, MDPI, 2017, 9, ⟨10.3390/rs9030258⟩ sea ice drift feature tracking pattern matching Sentinel-1 SAR [SDU]Sciences of the Universe [physics] info:eu-repo/semantics/article Journal articles 2017 ftccsdartic https://doi.org/10.3390/rs9030258 2021-11-06T23:27:49Z International audience Sea ice drift strongly influences sea ice thickness distribution and indirectly controls air-sea ice-ocean interactions. Estimating sea ice drift over a large range of spatial and temporal scales is therefore needed to characterize the properties of sea ice dynamics and to better understand the ongoing changes of the climate in the polar regions. An efficient algorithm is developed for processing SAR data based on the combination of feature tracking (FT) and pattern matching (PM) techniques. The main advantage of the combination is that the FT rapidly provides the first guess estimate of ice drift in a few unevenly distributed keypoints, and PM accurately provides drift vectors on a regular or irregular grid. Thorough sensitivity analysis of the algorithm is performed, and optimal sets of parameters are suggested for retrieval of sea ice drift on various spatial and temporal scales. The algorithm has rather high accuracy (error is below 300 m) and high speed (the time for one image pair is 1 min), which opens new opportunities for studying sea ice kinematic processes. The ice drift can now be efficiently observed in the Lagrangian coordinate system on an irregular grid and, therefore, used for pointwise evaluation of the models running on unstructured meshes or for assimilation into Lagrangian models. Article in Journal/Newspaper Sea ice Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) Remote Sensing 9 3 258 |
institution |
Open Polar |
collection |
Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) |
op_collection_id |
ftccsdartic |
language |
English |
topic |
sea ice drift feature tracking pattern matching Sentinel-1 SAR [SDU]Sciences of the Universe [physics] |
spellingShingle |
sea ice drift feature tracking pattern matching Sentinel-1 SAR [SDU]Sciences of the Universe [physics] Korosov, Anton, Rampal, Pierre A Combination of Feature Tracking and Pattern Matching with Optimal Parametrization for Sea Ice Drift Retrieval from SAR Data |
topic_facet |
sea ice drift feature tracking pattern matching Sentinel-1 SAR [SDU]Sciences of the Universe [physics] |
description |
International audience Sea ice drift strongly influences sea ice thickness distribution and indirectly controls air-sea ice-ocean interactions. Estimating sea ice drift over a large range of spatial and temporal scales is therefore needed to characterize the properties of sea ice dynamics and to better understand the ongoing changes of the climate in the polar regions. An efficient algorithm is developed for processing SAR data based on the combination of feature tracking (FT) and pattern matching (PM) techniques. The main advantage of the combination is that the FT rapidly provides the first guess estimate of ice drift in a few unevenly distributed keypoints, and PM accurately provides drift vectors on a regular or irregular grid. Thorough sensitivity analysis of the algorithm is performed, and optimal sets of parameters are suggested for retrieval of sea ice drift on various spatial and temporal scales. The algorithm has rather high accuracy (error is below 300 m) and high speed (the time for one image pair is 1 min), which opens new opportunities for studying sea ice kinematic processes. The ice drift can now be efficiently observed in the Lagrangian coordinate system on an irregular grid and, therefore, used for pointwise evaluation of the models running on unstructured meshes or for assimilation into Lagrangian models. |
author2 |
Nansen Environmental and Remote Sensing Center Bergen (NERSC) |
format |
Article in Journal/Newspaper |
author |
Korosov, Anton, Rampal, Pierre |
author_facet |
Korosov, Anton, Rampal, Pierre |
author_sort |
Korosov, Anton, |
title |
A Combination of Feature Tracking and Pattern Matching with Optimal Parametrization for Sea Ice Drift Retrieval from SAR Data |
title_short |
A Combination of Feature Tracking and Pattern Matching with Optimal Parametrization for Sea Ice Drift Retrieval from SAR Data |
title_full |
A Combination of Feature Tracking and Pattern Matching with Optimal Parametrization for Sea Ice Drift Retrieval from SAR Data |
title_fullStr |
A Combination of Feature Tracking and Pattern Matching with Optimal Parametrization for Sea Ice Drift Retrieval from SAR Data |
title_full_unstemmed |
A Combination of Feature Tracking and Pattern Matching with Optimal Parametrization for Sea Ice Drift Retrieval from SAR Data |
title_sort |
combination of feature tracking and pattern matching with optimal parametrization for sea ice drift retrieval from sar data |
publisher |
HAL CCSD |
publishDate |
2017 |
url |
https://hal.univ-grenoble-alpes.fr/hal-03405346 https://hal.univ-grenoble-alpes.fr/hal-03405346/document https://hal.univ-grenoble-alpes.fr/hal-03405346/file/Korosov2017aRemote_Sensing.pdf https://doi.org/10.3390/rs9030258 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
ISSN: 2072-4292 Remote Sensing https://hal.univ-grenoble-alpes.fr/hal-03405346 Remote Sensing, MDPI, 2017, 9, ⟨10.3390/rs9030258⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.3390/rs9030258 hal-03405346 https://hal.univ-grenoble-alpes.fr/hal-03405346 https://hal.univ-grenoble-alpes.fr/hal-03405346/document https://hal.univ-grenoble-alpes.fr/hal-03405346/file/Korosov2017aRemote_Sensing.pdf doi:10.3390/rs9030258 |
op_rights |
info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.3390/rs9030258 |
container_title |
Remote Sensing |
container_volume |
9 |
container_issue |
3 |
container_start_page |
258 |
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1766189684255358976 |