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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Bibliographic Details
Main Authors: Guan, Yawen, Sulsky, Deborah, Tucker, J. Derek, Sampson, Christian
Language:unknown
Published: 2022
Subjects:
58 GEOSCIENCES
54 ENVIRONMENTAL SCIENCES
97 MATHEMATICS AND COMPUTING
Arctic
Sea ice
Online Access:http://www.osti.gov/servlets/purl/1769711
https://www.osti.gov/biblio/1769711
https://doi.org/10.2172/1769711
id ftosti:oai:osti.gov:1769711
record_format openpolar
spelling 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
institution Open Polar
collection SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy)
op_collection_id ftosti
language unknown
topic 58 GEOSCIENCES
54 ENVIRONMENTAL SCIENCES
97 MATHEMATICS AND COMPUTING
spellingShingle 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

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