Sea Ice Classification: What Can We Learn From Airborne Data?

When asked what is important for sea ice classification, one of the most mentioned items is the large spatial coverage that can only be realized with satellites acquiring data in wide-swath or scanning image modes. Nevertheless, the performance of any sea ice classification algorithm is also influen...

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Main Author: Dierking, Wolfgang
Format: Conference Object
Language:unknown
Published: 2017
Subjects:
Online Access:https://epic.awi.de/id/eprint/45982/
https://hdl.handle.net/10013/epic.93cb0d2a-cbf8-447c-bcf4-f6e1303cb03e
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spelling ftawi:oai:epic.awi.de:45982 2024-09-15T18:34:17+00:00 Sea Ice Classification: What Can We Learn From Airborne Data? Dierking, Wolfgang 2017 https://epic.awi.de/id/eprint/45982/ https://hdl.handle.net/10013/epic.93cb0d2a-cbf8-447c-bcf4-f6e1303cb03e unknown Dierking, W. orcid:0000-0002-5031-648X (2017) Sea Ice Classification: What Can We Learn From Airborne Data? , CIRFA Seminar, University of Tromsø, Norway, 30 November 2017 - 30 November 2017 . hdl:10013/epic.93cb0d2a-cbf8-447c-bcf4-f6e1303cb03e EPIC3CIRFA Seminar, University of Tromsø, Norway, 2017-11-30-2017-11-30 Conference notRev 2017 ftawi 2024-06-24T04:18:50Z When asked what is important for sea ice classification, one of the most mentioned items is the large spatial coverage that can only be realized with satellites acquiring data in wide-swath or scanning image modes. Nevertheless, the performance of any sea ice classification algorithm is also influenced by the spatial resolution of the imaging instrument. The more details are visible, the easier it is for human analysts to separate different ice types. Hence, airborne SAR data, especially in conjunction with optical and/or thermal images, are extremely valuable for investigating the potential for sea ice classification with radar and to investigate the effects of a coarser spatial resolution typical for wide-swath satellite imagery. In this context, also the high-resolution imaging modes of modern satellite instruments have to be mentioned, which offer similar possibilities. In the presentation, examples from airborne radar and optical/thermal scanners are discussed, complemented by satellite data acquisitions, addressing specifically the potential advantage of using multi-frequency radar data for sea ice classification. Conference Object Sea ice Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center)
institution Open Polar
collection Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center)
op_collection_id ftawi
language unknown
description When asked what is important for sea ice classification, one of the most mentioned items is the large spatial coverage that can only be realized with satellites acquiring data in wide-swath or scanning image modes. Nevertheless, the performance of any sea ice classification algorithm is also influenced by the spatial resolution of the imaging instrument. The more details are visible, the easier it is for human analysts to separate different ice types. Hence, airborne SAR data, especially in conjunction with optical and/or thermal images, are extremely valuable for investigating the potential for sea ice classification with radar and to investigate the effects of a coarser spatial resolution typical for wide-swath satellite imagery. In this context, also the high-resolution imaging modes of modern satellite instruments have to be mentioned, which offer similar possibilities. In the presentation, examples from airborne radar and optical/thermal scanners are discussed, complemented by satellite data acquisitions, addressing specifically the potential advantage of using multi-frequency radar data for sea ice classification.
format Conference Object
author Dierking, Wolfgang
spellingShingle Dierking, Wolfgang
Sea Ice Classification: What Can We Learn From Airborne Data?
author_facet Dierking, Wolfgang
author_sort Dierking, Wolfgang
title Sea Ice Classification: What Can We Learn From Airborne Data?
title_short Sea Ice Classification: What Can We Learn From Airborne Data?
title_full Sea Ice Classification: What Can We Learn From Airborne Data?
title_fullStr Sea Ice Classification: What Can We Learn From Airborne Data?
title_full_unstemmed Sea Ice Classification: What Can We Learn From Airborne Data?
title_sort sea ice classification: what can we learn from airborne data?
publishDate 2017
url https://epic.awi.de/id/eprint/45982/
https://hdl.handle.net/10013/epic.93cb0d2a-cbf8-447c-bcf4-f6e1303cb03e
genre Sea ice
genre_facet Sea ice
op_source EPIC3CIRFA Seminar, University of Tromsø, Norway, 2017-11-30-2017-11-30
op_relation Dierking, W. orcid:0000-0002-5031-648X (2017) Sea Ice Classification: What Can We Learn From Airborne Data? , CIRFA Seminar, University of Tromsø, Norway, 30 November 2017 - 30 November 2017 . hdl:10013/epic.93cb0d2a-cbf8-447c-bcf4-f6e1303cb03e
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