Feature Detection
Focal Area(s): This proposal aims to develop and evaluate statistical models and machine learning algorithms for detecting and tracking features in spatiotemporal remotely sensed data with uncertainty quantification. We focus a particular application on the detection of sea ice leads and ridges in t...
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ftosti:oai:osti.gov:1769711 2023-07-30T04:01:24+02:00 Feature Detection Guan, Yawen Sulsky, Deborah Tucker, J. Derek Sampson, Christian 2022-01-27 application/pdf http://www.osti.gov/servlets/purl/1769711 https://www.osti.gov/biblio/1769711 https://doi.org/10.2172/1769711 unknown http://www.osti.gov/servlets/purl/1769711 https://www.osti.gov/biblio/1769711 https://doi.org/10.2172/1769711 doi:10.2172/1769711 58 GEOSCIENCES 54 ENVIRONMENTAL SCIENCES 97 MATHEMATICS AND COMPUTING 2022 ftosti https://doi.org/10.2172/1769711 2023-07-11T10:01:47Z Focal Area(s): This proposal aims to develop and evaluate statistical models and machine learning algorithms for detecting and tracking features in spatiotemporal remotely sensed data with uncertainty quantification. We focus a particular application on the detection of sea ice leads and ridges in the Arctic and use these key sea ice features for model calibration and to gain insight into the physics of sea ice thermodynamics and deformation. Other/Unknown Material Arctic Sea ice SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) Arctic |
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Open Polar |
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SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) |
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ftosti |
language |
unknown |
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58 GEOSCIENCES 54 ENVIRONMENTAL SCIENCES 97 MATHEMATICS AND COMPUTING |
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58 GEOSCIENCES 54 ENVIRONMENTAL SCIENCES 97 MATHEMATICS AND COMPUTING Guan, Yawen Sulsky, Deborah Tucker, J. Derek Sampson, Christian Feature Detection |
topic_facet |
58 GEOSCIENCES 54 ENVIRONMENTAL SCIENCES 97 MATHEMATICS AND COMPUTING |
description |
Focal Area(s): This proposal aims to develop and evaluate statistical models and machine learning algorithms for detecting and tracking features in spatiotemporal remotely sensed data with uncertainty quantification. We focus a particular application on the detection of sea ice leads and ridges in the Arctic and use these key sea ice features for model calibration and to gain insight into the physics of sea ice thermodynamics and deformation. |
author |
Guan, Yawen Sulsky, Deborah Tucker, J. Derek Sampson, Christian |
author_facet |
Guan, Yawen Sulsky, Deborah Tucker, J. Derek Sampson, Christian |
author_sort |
Guan, Yawen |
title |
Feature Detection |
title_short |
Feature Detection |
title_full |
Feature Detection |
title_fullStr |
Feature Detection |
title_full_unstemmed |
Feature Detection |
title_sort |
feature detection |
publishDate |
2022 |
url |
http://www.osti.gov/servlets/purl/1769711 https://www.osti.gov/biblio/1769711 https://doi.org/10.2172/1769711 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_relation |
http://www.osti.gov/servlets/purl/1769711 https://www.osti.gov/biblio/1769711 https://doi.org/10.2172/1769711 doi:10.2172/1769711 |
op_doi |
https://doi.org/10.2172/1769711 |
_version_ |
1772812136092794880 |