Velotto, Domenico and Tings, Björn and Bentes da Silva, Carlos Augusto (2017) Comparison of ship detectability between TerraSAR-X and Sentinel-1. In: Proceedings of the 3rd International Forum on Research and Technologies for Society and Industry (RTSI), pp. 1-5. IEEE. 3rd International Forum on Research and Technologies for Society and Industry (RTSI), 2017-09-11 - 2017-09-13, Modena, Italy. doi: 10.1109/RTSI.2017.8065913.
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Official URL: https://doi.org/10.1109/RTSI.2017.8065913
Abstract
In this paper, the detectability of ship signatures in Synthetic Aperture Radar (SAR) imagery acquired by the TerraSAR X/TanDEM-X and Sentinel 1 is compared. The comparison takes into account different sensors acquisition parameters and environmental conditions on a large variety of ship size and types. In the first step, ocean targets are detected using the Near Real Time (NRT)-optimized Constant-False-Alarm-Rate (CFAR) algorithm. The optimizations include the ocean/land and false targets discrimination. In the second step, all detected targets are automatically matched in space and time with the recorded Automatic Identification System (AIS) messages. A manual cross-check is performed at the end of the assignments to have a clean SAR ship signature database. Additionally, the local wind field is retrieved from the SAR backscatter of the ocean surface surrounding the detected ships, by applying the Geophysical Model Functions (GMF) inversion XMOD2 for X-band data and CMOD5 for C-band data. Similarly, the local sea state conditions are calculated by the XWAVE and CWAVE empirical model functions. The final detectability model takes into account all SAR-based information, i.e. wind speed and sea state, as well as relevant SAR parameters, e.g. incidence angle. The overall probability of detection are derived for three ship size categories, i.e. small, medium and large, adopting an L2-regularized Logistic Regression classifier trained on detected and nondetected ship samples.
| Item URL in elib: | https://elib.dlr.de/112265/ | ||||||||||||||||
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| Document Type: | Conference or Workshop Item (Speech) | ||||||||||||||||
| Additional Information: | Conference: http://rtsi2017.ieeesezioneitalia.it/tech_sess.html | ||||||||||||||||
| Title: | Comparison of ship detectability between TerraSAR-X and Sentinel-1 | ||||||||||||||||
| Authors: |
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| Date: | 12 October 2017 | ||||||||||||||||
| Journal or Publication Title: | Proceedings of the 3rd International Forum on Research and Technologies for Society and Industry (RTSI) | ||||||||||||||||
| Refereed publication: | Yes | ||||||||||||||||
| Open Access: | Yes | ||||||||||||||||
| Gold Open Access: | No | ||||||||||||||||
| In SCOPUS: | No | ||||||||||||||||
| In ISI Web of Science: | No | ||||||||||||||||
| DOI: | 10.1109/RTSI.2017.8065913 | ||||||||||||||||
| Page Range: | pp. 1-5 | ||||||||||||||||
| Publisher: | IEEE | ||||||||||||||||
| Series Name: | IEEE Xplore | ||||||||||||||||
| Status: | Published | ||||||||||||||||
| Keywords: | TerraSAR-X, Sentinel-1, SAR, ship detectability | ||||||||||||||||
| Event Title: | 3rd International Forum on Research and Technologies for Society and Industry (RTSI) | ||||||||||||||||
| Event Location: | Modena, Italy | ||||||||||||||||
| Event Type: | international Conference | ||||||||||||||||
| Event Start Date: | 11 September 2017 | ||||||||||||||||
| Event End Date: | 13 September 2017 | ||||||||||||||||
| Organizer: | IEEE | ||||||||||||||||
| 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 , Oberpfaffenhofen | ||||||||||||||||
| Institutes and Institutions: | Remote Sensing Technology Institute > SAR Signal Processing | ||||||||||||||||
| Deposited By: | Kaps, Ruth | ||||||||||||||||
| Deposited On: | 08 Dec 2017 11:30 | ||||||||||||||||
| Last Modified: | 24 Apr 2024 20:16 |
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