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Tings, Björn and Jacobsen, Sven and Wiehle, Stefan and Schwarz, Egbert and Daedelow, Holger (2021) X-Band/C-Band-Comparison of Ship Wake Detectability. In: 13th European Conference on Synthetic Aperture Radar, EUSAR 2021, pp. 700-704. IEEE. EUSAR 2021, 29. Mar - 01. Apr 2021, online. ISBN 978-3-8007-5457-1. ISSN 2197-4403.
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Official URL: https://ieeexplore.ieee.org/document/9472637
Abstract
This study presents an extension to recent ship wake detectability models based on SAR image analysis with machine learning. In contrast to our previous works, we model the detectability of certain wake components individually. The underlying data set is obtained by extracting possible ship wake signatures from SAR imagery by collocation with Automatic Identification System data. The developed detectability models are based on machine learning. They generally confirm previous findings based on simulated SAR data or qualitative image analysis. The results from our previous wake detectability model are compared to initial results from our new wake component detectability model.
| Item URL in elib: | https://elib.dlr.de/134596/ | ||||||||||||||||||
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| Document Type: | Conference or Workshop Item (Speech) | ||||||||||||||||||
| Title: | X-Band/C-Band-Comparison of Ship Wake Detectability | ||||||||||||||||||
| Authors: |
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| Date: | 31 March 2021 | ||||||||||||||||||
| Journal or Publication Title: | 13th European Conference on Synthetic Aperture Radar, EUSAR 2021 | ||||||||||||||||||
| Refereed publication: | Yes | ||||||||||||||||||
| Open Access: | No | ||||||||||||||||||
| Gold Open Access: | No | ||||||||||||||||||
| In SCOPUS: | Yes | ||||||||||||||||||
| In ISI Web of Science: | No | ||||||||||||||||||
| Page Range: | pp. 700-704 | ||||||||||||||||||
| Editors: |
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| Publisher: | IEEE | ||||||||||||||||||
| ISSN: | 2197-4403 | ||||||||||||||||||
| ISBN: | 978-3-8007-5457-1 | ||||||||||||||||||
| Status: | Published | ||||||||||||||||||
| Keywords: | Synthetic Aperture Radar; wake detection; detectability model; machine learning; Support Vector Machine; ocean surface imaging | ||||||||||||||||||
| Event Title: | EUSAR 2021 | ||||||||||||||||||
| Event Location: | online | ||||||||||||||||||
| Event Type: | international Conference | ||||||||||||||||||
| Event Dates: | 29. Mar - 01. Apr 2021 | ||||||||||||||||||
| Organizer: | VDE | ||||||||||||||||||
| HGF - Research field: | Aeronautics, Space and Transport | ||||||||||||||||||
| HGF - Program: | Space | ||||||||||||||||||
| HGF - Program Themes: | Earth Observation | ||||||||||||||||||
| DLR - Research area: | Raumfahrt | ||||||||||||||||||
| DLR - Program: | R EO - Earth Observation | ||||||||||||||||||
| DLR - Research theme (Project): | R - SAR methods | ||||||||||||||||||
| Location: | Bremen , Neustrelitz , Oberpfaffenhofen | ||||||||||||||||||
| Institutes and Institutions: | Remote Sensing Technology Institute > SAR Signal Processing German Remote Sensing Data Center > National Ground Segment | ||||||||||||||||||
| Deposited By: | Kaps, Ruth | ||||||||||||||||||
| Deposited On: | 27 Nov 2020 10:28 | ||||||||||||||||||
| Last Modified: | 13 Jul 2021 13:54 |
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