Automated polar ice thickness estimation from radar imagery

Abstract—This paper focuses on automating the task of esti-mating Polar ice thickness from airborne radar data acquired over Greenland and Antarctica. This process involves the identification and accurate selection of the ice sheet’s surface location and interface between the ice sheet and the under...

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Main Authors: Christopher M. Gifford, Gladys Finyom, Michael Jefferson, Student Member, Myasia Reid, Eric L. Akers, Arvin Agah, Senior Member
Other Authors: The Pennsylvania State University CiteSeerX Archives
Format: Text
Language:English
Published: 2010
Subjects:
Online Access:http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.652.1023
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spelling ftciteseerx:oai:CiteSeerX.psu:10.1.1.652.1023 2023-05-15T14:02:50+02:00 Automated polar ice thickness estimation from radar imagery Christopher M. Gifford Gladys Finyom Michael Jefferson Student Member Myasia Reid Eric L. Akers Arvin Agah Senior Member The Pennsylvania State University CiteSeerX Archives 2010 application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.652.1023 en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.652.1023 Metadata may be used without restrictions as long as the oai identifier remains attached to it. https://people.cresis.ku.edu/%7Ecgifford/Papers/Gifford_TIP_2010.pdf text 2010 ftciteseerx 2016-01-08T16:26:23Z Abstract—This paper focuses on automating the task of esti-mating Polar ice thickness from airborne radar data acquired over Greenland and Antarctica. This process involves the identification and accurate selection of the ice sheet’s surface location and interface between the ice sheet and the underlying bedrock for each measurement. Identifying the surface and bedrock locations in the radar imagery enables the computation of ice sheet thick-ness, which is important for the study of ice sheets, their volume, and how they may contribute to global climate change. The time-consuming manual approach requires sparse hand-selection of surface and bedrock interfaces by several human experts, and interpolating between the selections to save time. Two primary methods have been studied in this paper, namely, edge-based and active contour. Results are evaluated and presented using the metrics of time requirements and accuracy. Automated ice thickness estimation results from 2006 and 2007 Greenland field campaigns illustrate that the edge-based approach offers faster processing (seconds compared to minutes), but suffers from a lack of continuity and smoothness aspects that active contours provide. The active contour approach is more accurate when compared to ground truth selections provided by human experts, and has proven to be more robust to image artifacts. It is shown that both techniques offer advantages which could be integrated to yield a more effective system. Index Terms—Active contour, automated processing, edge detec-tion, polar ice thickness, radar data processing. I. Text Antarc* Antarctica Greenland Ice Sheet Unknown Greenland
institution Open Polar
collection Unknown
op_collection_id ftciteseerx
language English
description Abstract—This paper focuses on automating the task of esti-mating Polar ice thickness from airborne radar data acquired over Greenland and Antarctica. This process involves the identification and accurate selection of the ice sheet’s surface location and interface between the ice sheet and the underlying bedrock for each measurement. Identifying the surface and bedrock locations in the radar imagery enables the computation of ice sheet thick-ness, which is important for the study of ice sheets, their volume, and how they may contribute to global climate change. The time-consuming manual approach requires sparse hand-selection of surface and bedrock interfaces by several human experts, and interpolating between the selections to save time. Two primary methods have been studied in this paper, namely, edge-based and active contour. Results are evaluated and presented using the metrics of time requirements and accuracy. Automated ice thickness estimation results from 2006 and 2007 Greenland field campaigns illustrate that the edge-based approach offers faster processing (seconds compared to minutes), but suffers from a lack of continuity and smoothness aspects that active contours provide. The active contour approach is more accurate when compared to ground truth selections provided by human experts, and has proven to be more robust to image artifacts. It is shown that both techniques offer advantages which could be integrated to yield a more effective system. Index Terms—Active contour, automated processing, edge detec-tion, polar ice thickness, radar data processing. I.
author2 The Pennsylvania State University CiteSeerX Archives
format Text
author Christopher M. Gifford
Gladys Finyom
Michael Jefferson
Student Member
Myasia Reid
Eric L. Akers
Arvin Agah
Senior Member
spellingShingle Christopher M. Gifford
Gladys Finyom
Michael Jefferson
Student Member
Myasia Reid
Eric L. Akers
Arvin Agah
Senior Member
Automated polar ice thickness estimation from radar imagery
author_facet Christopher M. Gifford
Gladys Finyom
Michael Jefferson
Student Member
Myasia Reid
Eric L. Akers
Arvin Agah
Senior Member
author_sort Christopher M. Gifford
title Automated polar ice thickness estimation from radar imagery
title_short Automated polar ice thickness estimation from radar imagery
title_full Automated polar ice thickness estimation from radar imagery
title_fullStr Automated polar ice thickness estimation from radar imagery
title_full_unstemmed Automated polar ice thickness estimation from radar imagery
title_sort automated polar ice thickness estimation from radar imagery
publishDate 2010
url http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.652.1023
geographic Greenland
geographic_facet Greenland
genre Antarc*
Antarctica
Greenland
Ice Sheet
genre_facet Antarc*
Antarctica
Greenland
Ice Sheet
op_source https://people.cresis.ku.edu/%7Ecgifford/Papers/Gifford_TIP_2010.pdf
op_relation http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.652.1023
op_rights Metadata may be used without restrictions as long as the oai identifier remains attached to it.
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