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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Bibliographic Details
Main Authors: Sola, Daniel, Scott, K Andrea
Format: Article in Journal/Newspaper
Language:English
Published: University of Waterloo (Waterloo, Ontario, Canada) 2020
Subjects:
Hudson
Hudson Strait
Sea ice
Online Access:https://openjournals.uwaterloo.ca/index.php/vsl/article/view/1643
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spelling ftuniwaterlooojs:oai:canadianfoodstudies.uwaterloo.ca/index.php/cfs/oai:article/1643 2023-05-15T16:35:37+02:00 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 2022-05-09T15:36:13Z 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) Hudson Hudson Strait ENVELOPE(-70.000,-70.000,62.000,62.000)
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
long_lat ENVELOPE(-70.000,-70.000,62.000,62.000)
geographic Hudson
Hudson Strait
geographic_facet Hudson
Hudson Strait
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_ 1766025867117461504

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