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

Abstract

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.

Multiparametric sea state fields from synthetic aperture radar for maritime situational awareness

“…Furthermore, the fusion of oceanographic parameters not only generates additional value for MSA, but can also be applied for forecast model assimilation and to gain insights into the underlying physics. For instance, ship motion information from the Automatic Identification System (AIS) and background sea state information retrieved from SAR were combined in ship detectability models to connect the probability of SAR ship detection with the actual wind and sea state conditions (Tings et al, 2018a(Tings et al, , 2018b(Tings et al, , 2019Tings, 2021).…”

Modeling and SAR imaging of the sea surface: A review of the state-of-the-art with simulations

“…When considering superposition, it is important to select the correct wind velocity value for modeling. In many studies [5]- [8], [61], [71], [97], [130]- [132], it is postulated that wind velocity (or consequently significant wave height) is one of the main factors in ship wake visualization in SAR imagery. The Kelvin wake system (divergent and transverse waves) can be best observed when the wind velocity is about 3 m/s or less, as these levels give a very calm sea surface [7].…”

Pre-Processing of Simulated Synthetic Aperture Radar Image Scenes Using Polarimetric Enhancement for Improved Ship Wake Detection

“…Detecting ship wake features in synthetic aperture radar (SAR) imagery can provide an alternative way to obtain marine ship information, but it often becomes unavailable and unreliable for small, high-speed and stealthy marine ship targets during a high sea state [1]. Polarimetric SAR (PolSAR) can characterize the polarimetric properties of target backscatter and enhance the ship wake features, therefore it provides a potential way to solve this problem.…”