Proof of Concept for Sea Ice Stage of Development Classification Using Deep Learning
Accurate maps of ice concentration and ice type are needed to address increased interest in commercial marine transportation through the Arctic. RADARSAT-2 SAR imagery is the primary source of data used by expert ice analysts at the Canadian Ice Service (CIS) to produce sea ice maps over the Canadia...
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ftdoajarticles:oai:doaj.org/article:8c71ed265391457b85d84fdf2bebd945 2023-05-15T14:56:05+02:00 Proof of Concept for Sea Ice Stage of Development Classification Using Deep Learning Ryan Kruk M. Christopher Fuller Alexander S. Komarov Dustin Isleifson Ian Jeffrey 2020-08-01T00:00:00Z https://doi.org/10.3390/rs12152486 https://doaj.org/article/8c71ed265391457b85d84fdf2bebd945 EN eng MDPI AG https://www.mdpi.com/2072-4292/12/15/2486 https://doaj.org/toc/2072-4292 doi:10.3390/rs12152486 2072-4292 https://doaj.org/article/8c71ed265391457b85d84fdf2bebd945 Remote Sensing, Vol 12, Iss 2486, p 2486 (2020) sea ice Arctic Canadian sea ice chart deep learning SAR RADARSAT-2 Science Q article 2020 ftdoajarticles https://doi.org/10.3390/rs12152486 2022-12-31T09:43:42Z Accurate maps of ice concentration and ice type are needed to address increased interest in commercial marine transportation through the Arctic. RADARSAT-2 SAR imagery is the primary source of data used by expert ice analysts at the Canadian Ice Service (CIS) to produce sea ice maps over the Canadian territory. This study serves as a proof of concept that neural networks can be used to accurately predict ice type from SAR data. Datasets of SAR images served as inputs, and CIS ice charts served as labelled outputs to train a neural network to classify sea ice type. Our results show that DenseNet achieves the highest overall classification accuracy of 94.0% including water and the highest ice classification accuracy of 91.8% on a three class dataset using a fusion of HH and HV SAR polarizations for the input samples. The 91.8% ice classification accuracy validates the premise that a neural network can be used to effectively categorize different ice types based on SAR data. Article in Journal/Newspaper Arctic Sea ice Directory of Open Access Journals: DOAJ Articles Arctic Remote Sensing 12 15 2486 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
sea ice Arctic Canadian sea ice chart deep learning SAR RADARSAT-2 Science Q |
spellingShingle |
sea ice Arctic Canadian sea ice chart deep learning SAR RADARSAT-2 Science Q Ryan Kruk M. Christopher Fuller Alexander S. Komarov Dustin Isleifson Ian Jeffrey Proof of Concept for Sea Ice Stage of Development Classification Using Deep Learning |
topic_facet |
sea ice Arctic Canadian sea ice chart deep learning SAR RADARSAT-2 Science Q |
description |
Accurate maps of ice concentration and ice type are needed to address increased interest in commercial marine transportation through the Arctic. RADARSAT-2 SAR imagery is the primary source of data used by expert ice analysts at the Canadian Ice Service (CIS) to produce sea ice maps over the Canadian territory. This study serves as a proof of concept that neural networks can be used to accurately predict ice type from SAR data. Datasets of SAR images served as inputs, and CIS ice charts served as labelled outputs to train a neural network to classify sea ice type. Our results show that DenseNet achieves the highest overall classification accuracy of 94.0% including water and the highest ice classification accuracy of 91.8% on a three class dataset using a fusion of HH and HV SAR polarizations for the input samples. The 91.8% ice classification accuracy validates the premise that a neural network can be used to effectively categorize different ice types based on SAR data. |
format |
Article in Journal/Newspaper |
author |
Ryan Kruk M. Christopher Fuller Alexander S. Komarov Dustin Isleifson Ian Jeffrey |
author_facet |
Ryan Kruk M. Christopher Fuller Alexander S. Komarov Dustin Isleifson Ian Jeffrey |
author_sort |
Ryan Kruk |
title |
Proof of Concept for Sea Ice Stage of Development Classification Using Deep Learning |
title_short |
Proof of Concept for Sea Ice Stage of Development Classification Using Deep Learning |
title_full |
Proof of Concept for Sea Ice Stage of Development Classification Using Deep Learning |
title_fullStr |
Proof of Concept for Sea Ice Stage of Development Classification Using Deep Learning |
title_full_unstemmed |
Proof of Concept for Sea Ice Stage of Development Classification Using Deep Learning |
title_sort |
proof of concept for sea ice stage of development classification using deep learning |
publisher |
MDPI AG |
publishDate |
2020 |
url |
https://doi.org/10.3390/rs12152486 https://doaj.org/article/8c71ed265391457b85d84fdf2bebd945 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_source |
Remote Sensing, Vol 12, Iss 2486, p 2486 (2020) |
op_relation |
https://www.mdpi.com/2072-4292/12/15/2486 https://doaj.org/toc/2072-4292 doi:10.3390/rs12152486 2072-4292 https://doaj.org/article/8c71ed265391457b85d84fdf2bebd945 |
op_doi |
https://doi.org/10.3390/rs12152486 |
container_title |
Remote Sensing |
container_volume |
12 |
container_issue |
15 |
container_start_page |
2486 |
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1766328130177335296 |