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arXiv · 2402.04066

On the Modelling of Ship Wakes in S-Band SAR Images and an Application to Ship Identification

Kamirul Kamirul·Odysseas Pappas·Igor G. Rizaev·Alin Achim
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Abstract

We present a novel ship wake simulation system for generating S-band Synthetic Aperture Radar (SAR) images, and demonstrate the use of such imagery for the classification of ships based on their wake signatures via a deep learning approach. Ship wakes are modeled through the linear superposition of wind-induced sea elevation and the Kelvin wakes model of a moving ship. Our SAR imaging simulation takes into account frequency-dependent radar parameters, i.e., the complex dielectric constant ($\varepsilon$) and the relaxation rate ($\mu$) of seawater. The former was determined through the Debye model while the latter was estimated for S-band SAR based on pre-existing values for the L, C, and X-bands. The results show good agreement between simulated and real imagery upon visual inspection. The results of implementing different training strategies are also reported, showcasing a notable improvement in accuracy of classifier achieved by integrating real and simulated SAR images during the training.

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Kamirul Kamirul, Odysseas Pappas, Igor G. Rizaev, Alin Achim. 2024-02-06. On the Modelling of Ship Wakes in S-Band SAR Images and an Application to Ship Identification. https://arxiv.org/abs/2402.04066

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