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

arXiv: https://arxiv.org/abs/2402.04066 · HTML: https://mlsanity.com/paper/2402.04066/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2024.

## Built on by (most cited first, 3 of 3)

- [R-Sparse R-CNN: SAR Ship Detection Based on Background-Aware Sparse Learnable Proposals](https://mlsanity.com/paper/2504.18959/) (2025)
- [NASTaR: NovaSAR Automated Ship Target Recognition Dataset](https://mlsanity.com/paper/2512.18503/) (2025)
- [Physics-Informed Diffusion Models for SAR Ship Wake Generation from Text Prompts](https://mlsanity.com/paper/2504.20241/) (2025)

## Abstract (arXiv)

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 (μ) 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.