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...

Full description

Bibliographic Details
Main Authors: Montpetit, Benoit, Deschamps, Benjamin, King, Joshua, Duffe, Jason
Format: Article in Journal/Newspaper
Language:unknown
Published: Zenodo 2023
Subjects:
Online Access:https://dx.doi.org/10.5281/zenodo.8350643
https://zenodo.org/record/8350643
id ftdatacite:10.5281/zenodo.8350643
record_format openpolar
spelling ftdatacite:10.5281/zenodo.8350643 2023-11-05T03:39:56+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.8350643 https://zenodo.org/record/8350643 unknown Zenodo https://dx.doi.org/10.5281/zenodo.8350642 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.835064310.5281/zenodo.8350642 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.8350643
https://zenodo.org/record/8350643
genre Arctic
Coronation Gulf
Sea ice
genre_facet Arctic
Coronation Gulf
Sea ice
op_relation https://dx.doi.org/10.5281/zenodo.8350642
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.835064310.5281/zenodo.8350642
_version_ 1781695867746516992