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://elib.dlr.de/137052/ https://elib.dlr.de/137052/2/20_11_00_Singha_09257591.pdf https://doi.org/10.1109/TGRS.2020.3035029 |
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ftdlr:oai:elib.dlr.de:137052 2023-05-15T14:54:15+02:00 Robustness of SAR Sea Ice Type Classification across Incidence Angles and Seasons at L-band Singha, Suman Johansson, A. Malin Doulgeris, Anthony P. 2021-12 application/pdf https://elib.dlr.de/137052/ https://elib.dlr.de/137052/2/20_11_00_Singha_09257591.pdf https://doi.org/10.1109/TGRS.2020.3035029 en eng IEEE - Institute of Electrical and Electronics Engineers https://elib.dlr.de/137052/2/20_11_00_Singha_09257591.pdf Singha, Suman und Johansson, A. Malin und Doulgeris, Anthony P. (2021) Robustness of SAR Sea Ice Type Classification across Incidence Angles and Seasons at L-band. IEEE Transactions on Geoscience and Remote Sensing, 59 (12), Seiten 9941-9952. IEEE - Institute of Electrical and Electronics Engineers. doi:10.1109/TGRS.2020.3035029 <https://doi.org/10.1109/TGRS.2020.3035029>. ISSN 0196-2892. SAR-Signalverarbeitung Zeitschriftenbeitrag PeerReviewed 2021 ftdlr https://doi.org/10.1109/TGRS.2020.3035029 2022-03-27T23:10:38Z 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. Other Non-Article Part of Journal/Newspaper Arctic Sea ice German Aerospace Center: elib - DLR electronic library Arctic IEEE Transactions on Geoscience and Remote Sensing 1 12 |
institution |
Open Polar |
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German Aerospace Center: elib - DLR electronic library |
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ftdlr |
language |
English |
topic |
SAR-Signalverarbeitung |
spellingShingle |
SAR-Signalverarbeitung Singha, Suman Johansson, A. Malin Doulgeris, Anthony P. Robustness of SAR Sea Ice Type Classification across Incidence Angles and Seasons at L-band |
topic_facet |
SAR-Signalverarbeitung |
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 |
Other Non-Article Part of Journal/Newspaper |
author |
Singha, Suman Johansson, A. Malin Doulgeris, Anthony P. |
author_facet |
Singha, Suman Johansson, A. Malin Doulgeris, Anthony P. |
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 - Institute of Electrical and Electronics Engineers |
publishDate |
2021 |
url |
https://elib.dlr.de/137052/ https://elib.dlr.de/137052/2/20_11_00_Singha_09257591.pdf https://doi.org/10.1109/TGRS.2020.3035029 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_relation |
https://elib.dlr.de/137052/2/20_11_00_Singha_09257591.pdf Singha, Suman und Johansson, A. Malin und Doulgeris, Anthony P. (2021) Robustness of SAR Sea Ice Type Classification across Incidence Angles and Seasons at L-band. IEEE Transactions on Geoscience and Remote Sensing, 59 (12), Seiten 9941-9952. IEEE - Institute of Electrical and Electronics Engineers. doi:10.1109/TGRS.2020.3035029 <https://doi.org/10.1109/TGRS.2020.3035029>. ISSN 0196-2892. |
op_doi |
https://doi.org/10.1109/TGRS.2020.3035029 |
container_title |
IEEE Transactions on Geoscience and Remote Sensing |
container_start_page |
1 |
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12 |
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1766325981353607168 |