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

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...

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Bibliographic Details
Main Authors: Kamirul, Kamirul, Pappas, Odysseas, Rizaev, Igor G., Achim, Alin
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Dielectric constant
Inspection
Kelvin
NovaSAR-1
Radar imaging
S-band
SAR simulation
sea modelling
sea waves
Ship wakes
Training
vessel classification
Visualization
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Summary: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 preexisting 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.
ISSN:2153-7003
DOI:10.1109/IGARSS53475.2024.10642130