Assessing the Parameterization of RADARSAT-2 Dual-polarized ScanSAR Scenes on the Accuracy of a Convolutional Neural Network for Sea Ice Classification: Case Study over Coronation Gulf, Canada ...
Arctic amplification has many impacts on sea-ice extent, thickness, and flux. It becomes critical to monitor sea-ice conditions at finer spatio-temporal resolution. We used a simple convolutional neural network (CNN) on the RADARSAT-2 dual-polarized ScanSAR wide archive available over Coronation Gul...
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ftdatacite:10.5281/zenodo.8350642 2023-11-05T03:39:57+01:00 Assessing the Parameterization of RADARSAT-2 Dual-polarized ScanSAR Scenes on the Accuracy of a Convolutional Neural Network for Sea Ice Classification: Case Study over Coronation Gulf, Canada ... Montpetit, Benoit Deschamps, Benjamin King, Joshua Duffe, Jason 2023 https://dx.doi.org/10.5281/zenodo.8350642 https://zenodo.org/record/8350642 unknown Zenodo https://dx.doi.org/10.5281/zenodo.8350643 Open Access Open Government Licence - Canada https://open.canada.ca/en/open-government-licence-canada ogl-canada-2.0 info:eu-repo/semantics/openAccess Sea Ice, Convolutional Neural Network, Deep Learning, Synthetic Aperture Radar JournalArticle article-journal ScholarlyArticle 2023 ftdatacite https://doi.org/10.5281/zenodo.835064210.5281/zenodo.8350643 2023-10-09T10:59:56Z Arctic amplification has many impacts on sea-ice extent, thickness, and flux. It becomes critical to monitor sea-ice conditions at finer spatio-temporal resolution. We used a simple convolutional neural network (CNN) on the RADARSAT-2 dual-polarized ScanSAR wide archive available over Coronation Gulf, Canada, to assess which SAR parameter improves model performances to classify sea ice from water on a large volume of data covering 11 years of ice and surface water conditions. An overall accuracy of 90.1% was achieved on 989 scenes of 100% ice cover or ice-free conditions. An accuracy of 86.3% was achieved on the last year of data (134 scenes) which was kept out of the training process to test the model on an independent dataset. A better accuracy is obtained at lower incidence angles and the HH polarization provides the most information to classify ice from water. To achieve the best accuracy, the incidence angle and the noise equivalent sigma-nought had to be included as input to the model. A comparison ... : This dataset has been processed from RADARSAT-2 image products and saved into a stacked Python Numpy array. These arrays are analysis ready data to train/test the CNN model used in the referenced publication. Original RADARSAT-2 image products could not be shared directly since they are government by a End-User Licence Agreement (EULA). "RADARSAT-2 Data and Products © MacDONALD, DETTWILER and \n ASSOCIATES LTD (2023) - All Rights Reserved" and " RADARSAT is an official mark of the Canadian Space Agency" ... Article in Journal/Newspaper Arctic Coronation Gulf Sea ice DataCite Metadata Store (German National Library of Science and Technology) |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
topic |
Sea Ice, Convolutional Neural Network, Deep Learning, Synthetic Aperture Radar |
spellingShingle |
Sea Ice, Convolutional Neural Network, Deep Learning, Synthetic Aperture Radar Montpetit, Benoit Deschamps, Benjamin King, Joshua Duffe, Jason Assessing the Parameterization of RADARSAT-2 Dual-polarized ScanSAR Scenes on the Accuracy of a Convolutional Neural Network for Sea Ice Classification: Case Study over Coronation Gulf, Canada ... |
topic_facet |
Sea Ice, Convolutional Neural Network, Deep Learning, Synthetic Aperture Radar |
description |
Arctic amplification has many impacts on sea-ice extent, thickness, and flux. It becomes critical to monitor sea-ice conditions at finer spatio-temporal resolution. We used a simple convolutional neural network (CNN) on the RADARSAT-2 dual-polarized ScanSAR wide archive available over Coronation Gulf, Canada, to assess which SAR parameter improves model performances to classify sea ice from water on a large volume of data covering 11 years of ice and surface water conditions. An overall accuracy of 90.1% was achieved on 989 scenes of 100% ice cover or ice-free conditions. An accuracy of 86.3% was achieved on the last year of data (134 scenes) which was kept out of the training process to test the model on an independent dataset. A better accuracy is obtained at lower incidence angles and the HH polarization provides the most information to classify ice from water. To achieve the best accuracy, the incidence angle and the noise equivalent sigma-nought had to be included as input to the model. A comparison ... : This dataset has been processed from RADARSAT-2 image products and saved into a stacked Python Numpy array. These arrays are analysis ready data to train/test the CNN model used in the referenced publication. Original RADARSAT-2 image products could not be shared directly since they are government by a End-User Licence Agreement (EULA). "RADARSAT-2 Data and Products © MacDONALD, DETTWILER and \n ASSOCIATES LTD (2023) - All Rights Reserved" and " RADARSAT is an official mark of the Canadian Space Agency" ... |
format |
Article in Journal/Newspaper |
author |
Montpetit, Benoit Deschamps, Benjamin King, Joshua Duffe, Jason |
author_facet |
Montpetit, Benoit Deschamps, Benjamin King, Joshua Duffe, Jason |
author_sort |
Montpetit, Benoit |
title |
Assessing the Parameterization of RADARSAT-2 Dual-polarized ScanSAR Scenes on the Accuracy of a Convolutional Neural Network for Sea Ice Classification: Case Study over Coronation Gulf, Canada ... |
title_short |
Assessing the Parameterization of RADARSAT-2 Dual-polarized ScanSAR Scenes on the Accuracy of a Convolutional Neural Network for Sea Ice Classification: Case Study over Coronation Gulf, Canada ... |
title_full |
Assessing the Parameterization of RADARSAT-2 Dual-polarized ScanSAR Scenes on the Accuracy of a Convolutional Neural Network for Sea Ice Classification: Case Study over Coronation Gulf, Canada ... |
title_fullStr |
Assessing the Parameterization of RADARSAT-2 Dual-polarized ScanSAR Scenes on the Accuracy of a Convolutional Neural Network for Sea Ice Classification: Case Study over Coronation Gulf, Canada ... |
title_full_unstemmed |
Assessing the Parameterization of RADARSAT-2 Dual-polarized ScanSAR Scenes on the Accuracy of a Convolutional Neural Network for Sea Ice Classification: Case Study over Coronation Gulf, Canada ... |
title_sort |
assessing the parameterization of radarsat-2 dual-polarized scansar scenes on the accuracy of a convolutional neural network for sea ice classification: case study over coronation gulf, canada ... |
publisher |
Zenodo |
publishDate |
2023 |
url |
https://dx.doi.org/10.5281/zenodo.8350642 https://zenodo.org/record/8350642 |
genre |
Arctic Coronation Gulf Sea ice |
genre_facet |
Arctic Coronation Gulf Sea ice |
op_relation |
https://dx.doi.org/10.5281/zenodo.8350643 |
op_rights |
Open Access Open Government Licence - Canada https://open.canada.ca/en/open-government-licence-canada ogl-canada-2.0 info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.5281/zenodo.835064210.5281/zenodo.8350643 |
_version_ |
1781695883392319488 |