On the Modelling of Ship Wakes in S-Band SAR Images and an Application to Ship Identification
arxiv(2024)
摘要
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 (ε) 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.
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