Ship wake detection using Radon transforms of filtered SAR imagery

1996 ◽  
Author(s):  
Andrey Scherbakov ◽  
Ramon Hanssen ◽  
George Vosselman ◽  
Raymond Feron
Keyword(s):  
Radon Transforms ◽  
Ship Wake ◽  
Wake Detection ◽  
Sar Imagery

scholarly journals X-Band/C-Band-Comparison of Ship Wake Detectability

2020 ◽  
Author(s):  
Björn Tings ◽  
Sven Jacobsen ◽  
Stefan Wiehle ◽  
Egbert Schwarz ◽  
Holger Daedelow
Keyword(s):  
Detection Performance ◽  
Automatic Identification ◽  
Identification System ◽  
Sar Images ◽  
Ship Wake ◽  
X Band ◽  
System Data ◽  
Wake Detection ◽  
Sar Imagery ◽  
The Individual

Recent studies investigated the detectability of ship wake signatures on SAR imagery using a large number of SAR images collocated with Automatic Identification System data for training machine learning models. These detectability models are in agreement with oceanographic expectations from preceding studies and can therefore be used for comparing the performance of different SAR sensors in terms of wake detectability. Previous model comparisons showed better wake detection performance of TerraSAR-X (TS-X) than of RADARSAT-2 (RS2) and Sentinel-1 (S1). A comparison between CosmoSkymed (CSK) and RS2 is performed here, to examine the hypothesis that X-Band is generally better for wake detection than C-Band. Finally, this hypothesis is not confirmed, as the detectability models for TS-X, CSK and RS2 reveal similar performances. A comparison of wake detection performance should take the individual wake components into account separately.

scholarly journals Non-Linear Modeling of Detectability of Ship Wake Components in Dependency to Influencing Parameters Using Spaceborne X-Band SAR

2021 ◽  
Vol 13 (2) ◽  
pp. 165
Author(s):  
Björn Tings
Keyword(s):  
Incidence Angle ◽  
Support Vector ◽  
Wave Direction ◽  
Linear Modeling ◽  
Movement Velocity ◽  
Transverse Waves ◽  
Ship Wake ◽  
Ship Wakes ◽  
Wake Detection ◽  
Sar Imagery

The detection of the wakes of moving ships in Synthetic Aperture Radar (SAR) imagery requires the presence of wake signatures, which are sufficiently distinctive from the ocean background. Various wake components exist, which constitute the SAR signatures of ship wakes. For successful wake detection, the contrast between the detectable wake components and the background is crucial. The detectability of those wake components is affected by a number of parameters, which represent the image acquisition settings, environmental conditions or ship properties including voyage information. In this study the dependency of the detectability of individual wake components to these parameters is characterized. For each wake component a detectability model is built, which takes the influence of incidence angle, polarization, wind speed, wind direction, sea state (significant wave height, wavelength, wave direction), vessel’s velocity, vessel’s course over ground and vessel’s length into account. The presented detectability models are based on regression or classification using Support Vector Machines and a dataset of manually labelled TerraSAR‑X wake samples. The considered wake components are: near‑hull turbulences, turbulent wakes, Kelvin wake arms, Kelvin wake’s transverse waves, Kelvin wake’s divergent waves, V‑narrow wakes and ship‑generated internal waves. The statements derived about wake component detectability are mainly in good agreement with statements from previous research, but also some new assumptions are provided. The most expressive influencing parameter is the movement velocity of the vessels, as all wake components are more detectable the faster vessels move.

Localized radon transform for ship wake detection in SAR imagery

1994 ◽  
Author(s):  
Anthony C. Copeland ◽  
Gopalan Ravichandran ◽  
Mohan M. Trivedi
Keyword(s):  
Radon Transform ◽  
Ship Wake ◽  
Wake Detection ◽  
Sar Imagery

scholarly journals Ship Wake Detection in SAR Images via Sparse Regularization

2020 ◽  
Vol 58 (3) ◽  
pp. 1665-1677 ◽  
Author(s):  
Oktay Karakus ◽  
Igor Rizaev ◽  
Alin Achim
Keyword(s):  
Sar Images ◽  
Sparse Regularization ◽  
Ship Wake ◽  
Wake Detection

Faint Ship Wake Detection in PolSAR Images

2018 ◽  
Vol 15 (7) ◽  
pp. 1055-1059 ◽  
Author(s):  
Zhou Xu ◽  
Bo Tang ◽  
Shuiying Cheng
Keyword(s):  
Ship Wake ◽  
Wake Detection
Sign in / Sign up

Export Citation Format

Share Document