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...
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Online Access: | https://www.the-cryosphere.net/12/1307/2018/tc-12-1307-2018.pdf |
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doaj-9907bbb2e4334027a4090b71d244d5e02020-11-24T22:32:38ZengCopernicus PublicationsThe Cryosphere1994-04161994-04242018-04-01121307132910.5194/tc-12-1307-2018Open-source algorithm for detecting sea ice surface features in high-resolution optical imageryN. C. Wright0C. M. Polashenski1C. M. Polashenski2Thayer School of Engineering, Dartmouth College, Hanover, NH, USAThayer School of Engineering, Dartmouth College, Hanover, NH, USAU.S. Army Cold Regions Research and Engineering Laboratories, Hanover, NH, USASnow, 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.https://www.the-cryosphere.net/12/1307/2018/tc-12-1307-2018.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
N. C. Wright C. M. Polashenski C. M. Polashenski |
spellingShingle |
N. C. Wright C. M. Polashenski C. M. Polashenski Open-source algorithm for detecting sea ice surface features in high-resolution optical imagery The Cryosphere |
author_facet |
N. C. Wright C. M. Polashenski 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 |
series |
The Cryosphere |
issn |
1994-0416 1994-0424 |
publishDate |
2018-04-01 |
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. |
url |
https://www.the-cryosphere.net/12/1307/2018/tc-12-1307-2018.pdf |
work_keys_str_mv |
AT ncwright opensourcealgorithmfordetectingseaicesurfacefeaturesinhighresolutionopticalimagery AT cmpolashenski opensourcealgorithmfordetectingseaicesurfacefeaturesinhighresolutionopticalimagery AT cmpolashenski opensourcealgorithmfordetectingseaicesurfacefeaturesinhighresolutionopticalimagery |
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