Improving Sea Ice Characterization in Dry Ice Winter Conditions Using Polarimetric Parameters from C- and L-Band SAR Data
Sea ice monitoring and classification is one of the main applications of Synthetic Aperture Radar (SAR) remote sensing. C-band SAR imagery is regarded as an optimal choice for sea ice applications; however, other SAR frequencies has not been extensively assessed. In this study, we evaluate the poten...
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ftmdpi:oai:mdpi.com:/2072-4292/9/12/1270/ 2023-08-20T04:09:42+02:00 Improving Sea Ice Characterization in Dry Ice Winter Conditions Using Polarimetric Parameters from C- and L-Band SAR Data Mohammed Dabboor Benoit Montpetit Stephen Howell Christian Haas agris 2017-12-07 application/pdf https://doi.org/10.3390/rs9121270 EN eng Multidisciplinary Digital Publishing Institute Ocean Remote Sensing https://dx.doi.org/10.3390/rs9121270 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 9; Issue 12; Pages: 1270 L-band SAR sea ice polarimetric parameters classification Text 2017 ftmdpi https://doi.org/10.3390/rs9121270 2023-07-31T21:18:34Z Sea ice monitoring and classification is one of the main applications of Synthetic Aperture Radar (SAR) remote sensing. C-band SAR imagery is regarded as an optimal choice for sea ice applications; however, other SAR frequencies has not been extensively assessed. In this study, we evaluate the potential of fully polarimetric L-band SAR imagery for monitoring and classifying sea ice during dry winter conditions compared to fully polarimetric C-band SAR. Twelve polarimetric SAR parameters are derived using sets of C- and L-band SAR imagery and the capabilities of the derived parameters for the discrimination between First Year Ice (FYI) and Old Ice (OI), which is considered to be a mixture of Second Year Ice (SYI) and Multiyear Ice (MYI), are investigated. Feature vectors of effective C- and L-band polarimetric parameters are extracted and used for sea ice classification. Results indicate that C-band SAR provides high classification accuracy (98.99%) of FYI and OI in comparison to the obtained accuracy using L-band SAR (82.17% and 81.85%), as expected. However, L-band SAR was found to classify only the MYI floes as OI, while merging both FYI and SYI into one separate class. This comes in contrary to C-band SAR, which classifies as OI both MYI and SYI. This indicates a new potential for discriminating SYI from MYI by combining C- and L-band SAR in dry ice winter conditions. Text Sea ice MDPI Open Access Publishing Remote Sensing 9 12 1270 |
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Open Polar |
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MDPI Open Access Publishing |
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ftmdpi |
language |
English |
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L-band SAR sea ice polarimetric parameters classification |
spellingShingle |
L-band SAR sea ice polarimetric parameters classification Mohammed Dabboor Benoit Montpetit Stephen Howell Christian Haas Improving Sea Ice Characterization in Dry Ice Winter Conditions Using Polarimetric Parameters from C- and L-Band SAR Data |
topic_facet |
L-band SAR sea ice polarimetric parameters classification |
description |
Sea ice monitoring and classification is one of the main applications of Synthetic Aperture Radar (SAR) remote sensing. C-band SAR imagery is regarded as an optimal choice for sea ice applications; however, other SAR frequencies has not been extensively assessed. In this study, we evaluate the potential of fully polarimetric L-band SAR imagery for monitoring and classifying sea ice during dry winter conditions compared to fully polarimetric C-band SAR. Twelve polarimetric SAR parameters are derived using sets of C- and L-band SAR imagery and the capabilities of the derived parameters for the discrimination between First Year Ice (FYI) and Old Ice (OI), which is considered to be a mixture of Second Year Ice (SYI) and Multiyear Ice (MYI), are investigated. Feature vectors of effective C- and L-band polarimetric parameters are extracted and used for sea ice classification. Results indicate that C-band SAR provides high classification accuracy (98.99%) of FYI and OI in comparison to the obtained accuracy using L-band SAR (82.17% and 81.85%), as expected. However, L-band SAR was found to classify only the MYI floes as OI, while merging both FYI and SYI into one separate class. This comes in contrary to C-band SAR, which classifies as OI both MYI and SYI. This indicates a new potential for discriminating SYI from MYI by combining C- and L-band SAR in dry ice winter conditions. |
format |
Text |
author |
Mohammed Dabboor Benoit Montpetit Stephen Howell Christian Haas |
author_facet |
Mohammed Dabboor Benoit Montpetit Stephen Howell Christian Haas |
author_sort |
Mohammed Dabboor |
title |
Improving Sea Ice Characterization in Dry Ice Winter Conditions Using Polarimetric Parameters from C- and L-Band SAR Data |
title_short |
Improving Sea Ice Characterization in Dry Ice Winter Conditions Using Polarimetric Parameters from C- and L-Band SAR Data |
title_full |
Improving Sea Ice Characterization in Dry Ice Winter Conditions Using Polarimetric Parameters from C- and L-Band SAR Data |
title_fullStr |
Improving Sea Ice Characterization in Dry Ice Winter Conditions Using Polarimetric Parameters from C- and L-Band SAR Data |
title_full_unstemmed |
Improving Sea Ice Characterization in Dry Ice Winter Conditions Using Polarimetric Parameters from C- and L-Band SAR Data |
title_sort |
improving sea ice characterization in dry ice winter conditions using polarimetric parameters from c- and l-band sar data |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2017 |
url |
https://doi.org/10.3390/rs9121270 |
op_coverage |
agris |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
Remote Sensing; Volume 9; Issue 12; Pages: 1270 |
op_relation |
Ocean Remote Sensing https://dx.doi.org/10.3390/rs9121270 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs9121270 |
container_title |
Remote Sensing |
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9 |
container_issue |
12 |
container_start_page |
1270 |
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1774723321709985792 |