Identifying Sea Ice Ridging in SAR Imagery using various Machine Learning Models
Sea ice ridging presents a great challenge to ships navigating thearctic. In this paper, we examine the capabilities of various machinelearning methods in predicting regions of high ridge density fromSAR imagery of Hudson Strait. Our results showed that althoughridging in Hudson Strait may be diffic...
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University of Waterloo (Waterloo, Ontario, Canada)
2020
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ftuniwaterlooojs:oai:openjournals.uwaterloo.ca:article/1643 2024-09-15T18:11:06+00:00 Identifying Sea Ice Ridging in SAR Imagery using various Machine Learning Models Sola, Daniel Scott, K Andrea 2020-01-02 application/pdf https://openjournals.uwaterloo.ca/index.php/vsl/article/view/1643 eng eng University of Waterloo (Waterloo, Ontario, Canada) https://openjournals.uwaterloo.ca/index.php/vsl/article/view/1643/2013 https://openjournals.uwaterloo.ca/index.php/vsl/article/view/1643 Journal of Computational Vision and Imaging Systems; Vol. 5 No. 1 (2019): Special Issue: Proceedings of CVIS 2019; 1 2562-0444 10.15353/jcvis.v5i1 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article 2020 ftuniwaterlooojs https://doi.org/10.15353/jcvis.v5i1 2024-07-26T03:04:00Z Sea ice ridging presents a great challenge to ships navigating thearctic. In this paper, we examine the capabilities of various machinelearning methods in predicting regions of high ridge density fromSAR imagery of Hudson Strait. Our results showed that althoughridging in Hudson Strait may be difficult to distinguish even with thehuman eye, machine learning can give some insight into potentiallydangerous regions of Hudson Strait. Article in Journal/Newspaper Hudson Strait Sea ice Waterloo Library Journal Publishing Service (University of Waterloo, Canada) |
institution |
Open Polar |
collection |
Waterloo Library Journal Publishing Service (University of Waterloo, Canada) |
op_collection_id |
ftuniwaterlooojs |
language |
English |
description |
Sea ice ridging presents a great challenge to ships navigating thearctic. In this paper, we examine the capabilities of various machinelearning methods in predicting regions of high ridge density fromSAR imagery of Hudson Strait. Our results showed that althoughridging in Hudson Strait may be difficult to distinguish even with thehuman eye, machine learning can give some insight into potentiallydangerous regions of Hudson Strait. |
format |
Article in Journal/Newspaper |
author |
Sola, Daniel Scott, K Andrea |
spellingShingle |
Sola, Daniel Scott, K Andrea Identifying Sea Ice Ridging in SAR Imagery using various Machine Learning Models |
author_facet |
Sola, Daniel Scott, K Andrea |
author_sort |
Sola, Daniel |
title |
Identifying Sea Ice Ridging in SAR Imagery using various Machine Learning Models |
title_short |
Identifying Sea Ice Ridging in SAR Imagery using various Machine Learning Models |
title_full |
Identifying Sea Ice Ridging in SAR Imagery using various Machine Learning Models |
title_fullStr |
Identifying Sea Ice Ridging in SAR Imagery using various Machine Learning Models |
title_full_unstemmed |
Identifying Sea Ice Ridging in SAR Imagery using various Machine Learning Models |
title_sort |
identifying sea ice ridging in sar imagery using various machine learning models |
publisher |
University of Waterloo (Waterloo, Ontario, Canada) |
publishDate |
2020 |
url |
https://openjournals.uwaterloo.ca/index.php/vsl/article/view/1643 |
genre |
Hudson Strait Sea ice |
genre_facet |
Hudson Strait Sea ice |
op_source |
Journal of Computational Vision and Imaging Systems; Vol. 5 No. 1 (2019): Special Issue: Proceedings of CVIS 2019; 1 2562-0444 10.15353/jcvis.v5i1 |
op_relation |
https://openjournals.uwaterloo.ca/index.php/vsl/article/view/1643/2013 https://openjournals.uwaterloo.ca/index.php/vsl/article/view/1643 |
op_doi |
https://doi.org/10.15353/jcvis.v5i1 |
_version_ |
1810448694479159296 |