Robustness of SAR Sea Ice Type classification across incidence angles and seasons at L-band
In recent years, space-borne synthetic aperture radar (SAR) polarimetry has become a valuable tool for sea ice type retrieval. L-band SAR has proven to be sensitive toward deformed sea ice and is complementary compared with operationally used C-band SAR for sea ice type classification during the ear...
Published in: | IEEE Transactions on Geoscience and Remote Sensing |
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Online Access: | https://hdl.handle.net/10037/20244 https://doi.org/10.1109/TGRS.2020.3035029 |
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ftunivtroemsoe:oai:munin.uit.no:10037/20244 2023-05-15T14:27:53+02:00 Robustness of SAR Sea Ice Type classification across incidence angles and seasons at L-band Singha, Suman Johansson, Malin Doulgeris, Anthony Paul 2020-11-16 https://hdl.handle.net/10037/20244 https://doi.org/10.1109/TGRS.2020.3035029 eng eng IEEE IEEE Transactions on Geoscience and Remote Sensing info:eu-repo/grantAgreement/RCN/SFI/237906/Norway/Centre for Integrated Remote Sensing and Forecasting for Arctic Operations/CIRFA/ info:eu-repo/grantAgreement/RCN/PETROMAKS2/280616/Norway/Oil spill and newly formed sea ice detection, characterization, and mapping in the Barents Sea using remote sensing by SAR// Singha S, Johansson A M, Doulgeris ap. Robustness of SAR Sea Ice Type classification across incidence angles and seasons at L-band. IEEE Transactions on Geoscience and Remote Sensing. 2020 FRIDAID 1841803 doi:10.1109/TGRS.2020.3035029 0196-2892 1558-0644 https://hdl.handle.net/10037/20244 openAccess Copyright 2020 The Author(s) VDP::Technology: 500::Information and communication technology: 550 VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550 Journal article Tidsskriftartikkel publishedVersion 2020 ftunivtroemsoe https://doi.org/10.1109/TGRS.2020.3035029 2021-06-25T17:57:53Z In recent years, space-borne synthetic aperture radar (SAR) polarimetry has become a valuable tool for sea ice type retrieval. L-band SAR has proven to be sensitive toward deformed sea ice and is complementary compared with operationally used C-band SAR for sea ice type classification during the early and advanced melt seasons. Here, we employ an artificial neural network (ANN)-based sea ice type classification algorithm on a comprehensive data set of ALOS-2 PALSAR-2 fully polarimetric images acquired with a range of incidence angles and during different environmental conditions. The variability within the data set means that it is ideal for making a novel assessment of the robustness of the sea ice classification, investigating the intraclass variability, the seasonal variations, and the incidence angle effect on the sea ice classification results. The images coincide with two different Arctic campaigns in 2015: the Norwegian Young Sea Ice Cruise 2015 (N-ICE2015) and the Polarstern's (PS92) Transitions in the Arctic Seasonal Sea Ice Zone (TRANSSIZ). We find that it is essential to take into account seasonality and intraclass variability when establishing training data for machine learning-based algorithms though moderate differences in incidence angle are possible to accommodate by the classifier during the dry and cold winter season. We also conclude that the incidence angle dependence of backscatter for a given ice type is consistent for different Arctic regions. Article in Journal/Newspaper Arctic Arctic Sea ice University of Tromsø: Munin Open Research Archive Arctic IEEE Transactions on Geoscience and Remote Sensing 1 12 |
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University of Tromsø: Munin Open Research Archive |
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ftunivtroemsoe |
language |
English |
topic |
VDP::Technology: 500::Information and communication technology: 550 VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550 |
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VDP::Technology: 500::Information and communication technology: 550 VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550 Singha, Suman Johansson, Malin Doulgeris, Anthony Paul Robustness of SAR Sea Ice Type classification across incidence angles and seasons at L-band |
topic_facet |
VDP::Technology: 500::Information and communication technology: 550 VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550 |
description |
In recent years, space-borne synthetic aperture radar (SAR) polarimetry has become a valuable tool for sea ice type retrieval. L-band SAR has proven to be sensitive toward deformed sea ice and is complementary compared with operationally used C-band SAR for sea ice type classification during the early and advanced melt seasons. Here, we employ an artificial neural network (ANN)-based sea ice type classification algorithm on a comprehensive data set of ALOS-2 PALSAR-2 fully polarimetric images acquired with a range of incidence angles and during different environmental conditions. The variability within the data set means that it is ideal for making a novel assessment of the robustness of the sea ice classification, investigating the intraclass variability, the seasonal variations, and the incidence angle effect on the sea ice classification results. The images coincide with two different Arctic campaigns in 2015: the Norwegian Young Sea Ice Cruise 2015 (N-ICE2015) and the Polarstern's (PS92) Transitions in the Arctic Seasonal Sea Ice Zone (TRANSSIZ). We find that it is essential to take into account seasonality and intraclass variability when establishing training data for machine learning-based algorithms though moderate differences in incidence angle are possible to accommodate by the classifier during the dry and cold winter season. We also conclude that the incidence angle dependence of backscatter for a given ice type is consistent for different Arctic regions. |
format |
Article in Journal/Newspaper |
author |
Singha, Suman Johansson, Malin Doulgeris, Anthony Paul |
author_facet |
Singha, Suman Johansson, Malin Doulgeris, Anthony Paul |
author_sort |
Singha, Suman |
title |
Robustness of SAR Sea Ice Type classification across incidence angles and seasons at L-band |
title_short |
Robustness of SAR Sea Ice Type classification across incidence angles and seasons at L-band |
title_full |
Robustness of SAR Sea Ice Type classification across incidence angles and seasons at L-band |
title_fullStr |
Robustness of SAR Sea Ice Type classification across incidence angles and seasons at L-band |
title_full_unstemmed |
Robustness of SAR Sea Ice Type classification across incidence angles and seasons at L-band |
title_sort |
robustness of sar sea ice type classification across incidence angles and seasons at l-band |
publisher |
IEEE |
publishDate |
2020 |
url |
https://hdl.handle.net/10037/20244 https://doi.org/10.1109/TGRS.2020.3035029 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Arctic Sea ice |
genre_facet |
Arctic Arctic Sea ice |
op_relation |
IEEE Transactions on Geoscience and Remote Sensing info:eu-repo/grantAgreement/RCN/SFI/237906/Norway/Centre for Integrated Remote Sensing and Forecasting for Arctic Operations/CIRFA/ info:eu-repo/grantAgreement/RCN/PETROMAKS2/280616/Norway/Oil spill and newly formed sea ice detection, characterization, and mapping in the Barents Sea using remote sensing by SAR// Singha S, Johansson A M, Doulgeris ap. Robustness of SAR Sea Ice Type classification across incidence angles and seasons at L-band. IEEE Transactions on Geoscience and Remote Sensing. 2020 FRIDAID 1841803 doi:10.1109/TGRS.2020.3035029 0196-2892 1558-0644 https://hdl.handle.net/10037/20244 |
op_rights |
openAccess Copyright 2020 The Author(s) |
op_doi |
https://doi.org/10.1109/TGRS.2020.3035029 |
container_title |
IEEE Transactions on Geoscience and Remote Sensing |
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1 |
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12 |
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1766301955288727552 |