Classification of Sea Ice Types in Sentinel-1 SAR Data Using Convolutional Neural Networks
A new algorithm for classification of sea ice types on Sentinel-1 Synthetic Aperture Radar (SAR) data using a convolutional neural network (CNN) is presented. The CNN is trained on reference ice charts produced by human experts and compared with an existing machine learning algorithm based on textur...
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Multidisciplinary Digital Publishing Institute
2020
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Online Access: | https://doi.org/10.3390/rs12132165 |
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ftmdpi:oai:mdpi.com:/2072-4292/12/13/2165/ 2023-08-20T04:04:34+02:00 Classification of Sea Ice Types in Sentinel-1 SAR Data Using Convolutional Neural Networks Hugo Boulze Anton Korosov Julien Brajard agris 2020-07-07 application/pdf https://doi.org/10.3390/rs12132165 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/rs12132165 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 12; Issue 13; Pages: 2165 convolutional neural network Sentinel-1 SAR sea ice type ice chart Arctic Text 2020 ftmdpi https://doi.org/10.3390/rs12132165 2023-07-31T23:44:21Z A new algorithm for classification of sea ice types on Sentinel-1 Synthetic Aperture Radar (SAR) data using a convolutional neural network (CNN) is presented. The CNN is trained on reference ice charts produced by human experts and compared with an existing machine learning algorithm based on texture features and random forest classifier. The CNN is trained on two datasets in 2018 and 2020 for retrieval of four classes: ice free, young ice, first-year ice and old ice. The accuracy of our classification is 90.5% for the 2018-dataset and 91.6% for the 2020-dataset. The uncertainty is a bit higher for young ice (85%/76% accuracy in 2018/2020) and first-year ice (86%/84% accuracy in 2018/2020). Our algorithm outperforms the existing random forest product for each ice type. It has also proved to be more efficient in computing time and less sensitive to the noise in SAR data. The code is publicly available. Text Arctic Sea ice MDPI Open Access Publishing Arctic Remote Sensing 12 13 2165 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
convolutional neural network Sentinel-1 SAR sea ice type ice chart Arctic |
spellingShingle |
convolutional neural network Sentinel-1 SAR sea ice type ice chart Arctic Hugo Boulze Anton Korosov Julien Brajard Classification of Sea Ice Types in Sentinel-1 SAR Data Using Convolutional Neural Networks |
topic_facet |
convolutional neural network Sentinel-1 SAR sea ice type ice chart Arctic |
description |
A new algorithm for classification of sea ice types on Sentinel-1 Synthetic Aperture Radar (SAR) data using a convolutional neural network (CNN) is presented. The CNN is trained on reference ice charts produced by human experts and compared with an existing machine learning algorithm based on texture features and random forest classifier. The CNN is trained on two datasets in 2018 and 2020 for retrieval of four classes: ice free, young ice, first-year ice and old ice. The accuracy of our classification is 90.5% for the 2018-dataset and 91.6% for the 2020-dataset. The uncertainty is a bit higher for young ice (85%/76% accuracy in 2018/2020) and first-year ice (86%/84% accuracy in 2018/2020). Our algorithm outperforms the existing random forest product for each ice type. It has also proved to be more efficient in computing time and less sensitive to the noise in SAR data. The code is publicly available. |
format |
Text |
author |
Hugo Boulze Anton Korosov Julien Brajard |
author_facet |
Hugo Boulze Anton Korosov Julien Brajard |
author_sort |
Hugo Boulze |
title |
Classification of Sea Ice Types in Sentinel-1 SAR Data Using Convolutional Neural Networks |
title_short |
Classification of Sea Ice Types in Sentinel-1 SAR Data Using Convolutional Neural Networks |
title_full |
Classification of Sea Ice Types in Sentinel-1 SAR Data Using Convolutional Neural Networks |
title_fullStr |
Classification of Sea Ice Types in Sentinel-1 SAR Data Using Convolutional Neural Networks |
title_full_unstemmed |
Classification of Sea Ice Types in Sentinel-1 SAR Data Using Convolutional Neural Networks |
title_sort |
classification of sea ice types in sentinel-1 sar data using convolutional neural networks |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2020 |
url |
https://doi.org/10.3390/rs12132165 |
op_coverage |
agris |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_source |
Remote Sensing; Volume 12; Issue 13; Pages: 2165 |
op_relation |
https://dx.doi.org/10.3390/rs12132165 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs12132165 |
container_title |
Remote Sensing |
container_volume |
12 |
container_issue |
13 |
container_start_page |
2165 |
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1774714956905709568 |