Open-source algorithm for detecting sea ice surface features in high-resolution optical imagery
Snow, ice, and melt ponds cover the surface of the Arctic Ocean in fractions that change throughout the seasons. These surfaces control albedo and exert tremendous influence over the energy balance in the Arctic. Increasingly available meter- to decimeter-scale resolution optical imagery captures th...
Published in: | The Cryosphere |
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Copernicus Publications
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Online Access: | https://doi.org/10.5194/tc-12-1307-2018 https://www.the-cryosphere.net/12/1307/2018/tc-12-1307-2018.pdf https://doaj.org/article/9907bbb2e4334027a4090b71d244d5e0 |
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fttriple:oai:gotriple.eu:oai:doaj.org/article:9907bbb2e4334027a4090b71d244d5e0 2023-05-15T13:11:37+02:00 Open-source algorithm for detecting sea ice surface features in high-resolution optical imagery N. C. Wright C. M. Polashenski 2018-04-01 https://doi.org/10.5194/tc-12-1307-2018 https://www.the-cryosphere.net/12/1307/2018/tc-12-1307-2018.pdf https://doaj.org/article/9907bbb2e4334027a4090b71d244d5e0 en eng Copernicus Publications doi:10.5194/tc-12-1307-2018 1994-0416 1994-0424 https://www.the-cryosphere.net/12/1307/2018/tc-12-1307-2018.pdf https://doaj.org/article/9907bbb2e4334027a4090b71d244d5e0 undefined The Cryosphere, Vol 12, Pp 1307-1329 (2018) geo envir Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2018 fttriple https://doi.org/10.5194/tc-12-1307-2018 2023-01-22T17:53:14Z Snow, ice, and melt ponds cover the surface of the Arctic Ocean in fractions that change throughout the seasons. These surfaces control albedo and exert tremendous influence over the energy balance in the Arctic. Increasingly available meter- to decimeter-scale resolution optical imagery captures the evolution of the ice and ocean surface state visually, but methods for quantifying coverage of key surface types from raw imagery are not yet well established. Here we present an open-source system designed to provide a standardized, automated, and reproducible technique for processing optical imagery of sea ice. The method classifies surface coverage into three main categories: snow and bare ice, melt ponds and submerged ice, and open water. The method is demonstrated on imagery from four sensor platforms and on imagery spanning from spring thaw to fall freeze-up. Tests show the classification accuracy of this method typically exceeds 96 %. To facilitate scientific use, we evaluate the minimum observation area required for reporting a representative sample of surface coverage. We provide an open-source distribution of this algorithm and associated training datasets and suggest the community consider this a step towards standardizing optical sea ice imagery processing. We hope to encourage future collaborative efforts to improve the code base and to analyze large datasets of optical sea ice imagery. Article in Journal/Newspaper albedo Arctic Arctic Ocean Sea ice The Cryosphere Unknown Arctic Arctic Ocean The Cryosphere 12 4 1307 1329 |
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English |
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geo envir |
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geo envir N. C. Wright C. M. Polashenski Open-source algorithm for detecting sea ice surface features in high-resolution optical imagery |
topic_facet |
geo envir |
description |
Snow, ice, and melt ponds cover the surface of the Arctic Ocean in fractions that change throughout the seasons. These surfaces control albedo and exert tremendous influence over the energy balance in the Arctic. Increasingly available meter- to decimeter-scale resolution optical imagery captures the evolution of the ice and ocean surface state visually, but methods for quantifying coverage of key surface types from raw imagery are not yet well established. Here we present an open-source system designed to provide a standardized, automated, and reproducible technique for processing optical imagery of sea ice. The method classifies surface coverage into three main categories: snow and bare ice, melt ponds and submerged ice, and open water. The method is demonstrated on imagery from four sensor platforms and on imagery spanning from spring thaw to fall freeze-up. Tests show the classification accuracy of this method typically exceeds 96 %. To facilitate scientific use, we evaluate the minimum observation area required for reporting a representative sample of surface coverage. We provide an open-source distribution of this algorithm and associated training datasets and suggest the community consider this a step towards standardizing optical sea ice imagery processing. We hope to encourage future collaborative efforts to improve the code base and to analyze large datasets of optical sea ice imagery. |
format |
Article in Journal/Newspaper |
author |
N. C. Wright C. M. Polashenski |
author_facet |
N. C. Wright C. M. Polashenski |
author_sort |
N. C. Wright |
title |
Open-source algorithm for detecting sea ice surface features in high-resolution optical imagery |
title_short |
Open-source algorithm for detecting sea ice surface features in high-resolution optical imagery |
title_full |
Open-source algorithm for detecting sea ice surface features in high-resolution optical imagery |
title_fullStr |
Open-source algorithm for detecting sea ice surface features in high-resolution optical imagery |
title_full_unstemmed |
Open-source algorithm for detecting sea ice surface features in high-resolution optical imagery |
title_sort |
open-source algorithm for detecting sea ice surface features in high-resolution optical imagery |
publisher |
Copernicus Publications |
publishDate |
2018 |
url |
https://doi.org/10.5194/tc-12-1307-2018 https://www.the-cryosphere.net/12/1307/2018/tc-12-1307-2018.pdf https://doaj.org/article/9907bbb2e4334027a4090b71d244d5e0 |
geographic |
Arctic Arctic Ocean |
geographic_facet |
Arctic Arctic Ocean |
genre |
albedo Arctic Arctic Ocean Sea ice The Cryosphere |
genre_facet |
albedo Arctic Arctic Ocean Sea ice The Cryosphere |
op_source |
The Cryosphere, Vol 12, Pp 1307-1329 (2018) |
op_relation |
doi:10.5194/tc-12-1307-2018 1994-0416 1994-0424 https://www.the-cryosphere.net/12/1307/2018/tc-12-1307-2018.pdf https://doaj.org/article/9907bbb2e4334027a4090b71d244d5e0 |
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undefined |
op_doi |
https://doi.org/10.5194/tc-12-1307-2018 |
container_title |
The Cryosphere |
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12 |
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4 |
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1307 |
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1329 |
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1766248207173550080 |