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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, 04.-07. Nov. 2018, Jeju Island, the Republic of Korea.

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

Abstract

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.

Item URL in elib:https://elib.dlr.de/121323/
Document Type:Conference or Workshop Item (Speech)
Title:Non-Linear Modelling of Detectability of Ship Wakes
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Tings, BjörnBjoern.Tings (at) dlr.dehttps://orcid.org/0000-0002-1945-6433
Date:November 2018
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:maritime object recognition, wake detection, Synthetic Aperture Radar, machine learning
Event Title:PORSEC 2018
Event Location:Jeju Island, the Republic of Korea
Event Type:international Conference
Event Dates:04.-07. Nov. 2018
Organizer:Porsec SOC
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - SAR-Methodology
Location: Bremen , Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > SAR Signal Processing
Deposited By: Kaps, Ruth
Deposited On:29 Nov 2018 11:26
Last Modified:30 Nov 2018 12:11

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