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Non-Linear Modelling of Detectability of Ship Wakes

Tings, Björn (2018) Non-Linear Modelling of Detectability of Ship Wakes. PORSEC 2018, 2018-11-04 - 2018-11-07, Jeju Island, the Republic of Korea.

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Offizielle URL: http://porsec2018.kosc.kr/

Kurzfassung

On PORSEC 2016 a linear model for the detectability of ship signatures and besides also wake signatures was presented. The published linear model is capable of either calculating the probability of detection for ships or of their wakes in dependency to three parameters out of a set of parameters, which quantify environmental conditions, image acquisition parameters and ship's properties. Due to the linear basis of the model not all parameters are considered appropriately. Therefore the existing model has been extended by a non-linear basis. The new model takes more than three parameters and also additional parameters into account. This time the focus of the paper lies on the detectability of ship's wake signatures visible on TerraSAR-X/TanDEM-X high resolution mode acquisitions. Information about the influence of the respective parameters to the detectability of wakes can be retrieved from the model. Further, in case of non-sparse input data, the model can be used for estimation of missing parameters. The final goal of the model is the control of sensitivity of an automatic wake detection algorithm. The model is based on machine learning; therefore the session "Machine learning applications to ocean satellite remote sensing" would match. As the focus lies on wake detection and TerraSAR-X' high resolution modes, also the session "Sea surface roughness from high resolution SAR" would be appropriate. Last but not least, due to the development goal of an automatic wake detection algorithm, the presentation can also be assigned to the "Operational oceanography" session.

elib-URL des Eintrags:https://elib.dlr.de/121323/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:Non-Linear Modelling of Detectability of Ship Wakes
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Tings, BjörnBjoern.Tings (at) dlr.dehttps://orcid.org/0000-0002-1945-6433NICHT SPEZIFIZIERT
Datum:November 2018
Referierte Publikation:Nein
Open Access:Nein
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
Status:veröffentlicht
Stichwörter:maritime object recognition, wake detection, Synthetic Aperture Radar, machine learning
Veranstaltungstitel:PORSEC 2018
Veranstaltungsort:Jeju Island, the Republic of Korea
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:4 November 2018
Veranstaltungsende:7 November 2018
Veranstalter :Porsec SOC
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Erdbeobachtung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):R - SAR-Methoden
Standort: Bremen , Oberpfaffenhofen
Institute & Einrichtungen:Institut für Methodik der Fernerkundung > SAR-Signalverarbeitung
Hinterlegt von: Kaps, Ruth
Hinterlegt am:29 Nov 2018 11:26
Letzte Änderung:24 Apr 2024 20:25

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