Modeling and SAR Imaging of the Sea Surface: a Review of the State-of-the-Art with Simulations

Rizaev, Igor G.; Karakuş, Oktay; Hogan, S. J.; Achim, Alin

doi:10.31223/x5sp5b

Cited by 8 publications

(11 citation statements)

References 128 publications

(195 reference statements)

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“…A complete description of the SAR imagery simulation methodology with all the relevant mathematical details is available in [7], with the corresponding open-source package (MATLAB) available via the University of Bristol Research Data Repository [22].…”

Section: Methodsmentioning

confidence: 99%

“…The second component of the SAR image, Z ship , is modeled as a Kelvin wake and is based on the Michell thin ship theory with its further approximated form of fluid velocity potential described in [4,7]. Based on freely available information at www.marinetraffic.com, we selected ten real ships (cargo, tanker, passenger vessel, high speed craft, fishing vessel) and modeled them using the parameters shown in Table 1.…”

Section: Ship Wake Modeling In Sar Imagerymentioning

confidence: 99%

“…Ship wakes provide key information for surveillance of maritime traffic (e.g., illegal commercial activities) and are also useful in classifying the characteristics of the wake generating vessel and hence estimating ship heading and velocity [3,4]. A detailed description of the SAR wake imaging mechanism is presented in [5][6][7]. In addition, the availability of automatic identification system (AIS) data enables the integration of such information for machine learning development since it can constitute the ground-truth for ship identification.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, attempting the acquisition of all possible real SAR image variants for each ship would be a gargantuan task, as many factors must be taken into accounts, such as different ship velocities and different sea states. The use of an available and versatile SAR image simulator [7,22] allows the generation of an unlimited number of different scenarios, overcoming these limitations. Thus, in our work, for the first time, we present and make openly available a synthetic dataset of SAR images containing ship wakes for classification purposes.…”

Section: Introductionmentioning

confidence: 99%

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SynthWakeSAR: A Synthetic SAR Dataset for Deep Learning Classification of Ships at Sea

Rizaev¹,

Achim²

2022

Preprint

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The classification of vessel types in SAR imagery is of crucial importance for maritime applications. However, the ability to use real SAR imagery for deep learning classification is limited, due to the general lack of such data and/or the labor-intensive nature of labeling them. Simulating SAR images can overcome these limitations, allowing the generation of an infinite number of datasets. In this contribution, we present a synthetic SAR imagery dataset with ship wakes, which comprises 46080 images for ten different real vessel models. The variety of simulation parameters includes 16 ship heading directions, 6 ship velocities, 8 wind directions, 2 wind velocities, and 3 incidence angles. In addition, we extensively investigate classification performance for noise-free, noisy, and denoised ship wake scenes. We utilize the standard AlexNet architecture and employ training from scratch. To achieve the best classification performance, we conduct Bayesian optimization to determine hyperparameters. Results demonstrate that the classification of vessel types based on their SAR signatures is highly efficient, with maximum accuracies of 96.16%, 92.7%, and 93.59%, when training using noise-free, noisy, and denoised datasets respectively. Thus, we conclude that the best strategy in practical applications should be to train convolutional neural networks on denoised SAR datasets. The results show that the versatility of the SAR simulator can open up new horizons in the application of machine learning to a variety of SAR platforms.

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Section: Methodsmentioning

confidence: 99%

Section: Ship Wake Modeling In Sar Imagerymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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SynthWakeSAR: A Synthetic SAR Dataset for Deep Learning Classification of Ships at Sea

Rizaev¹,

Achim²

2022

Preprint

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“…Migliaccio [13] experimentally analyzed the SAR ocean speckle intensity K-distribution model versus sea surface wind field. Rizaev [14] presented a universal simulation framework for SAR imagery of the sea surface based on the linear theory of sea surface modeling, Michell thin-ship theory for Kelvin wake modeling, and ocean SAR imaging theory. Xu [15] deeply studied the simulation of SAR echo signal in complex ocean environment by using the four-path model and high frequency calculation method.…”

Section: Introductionmentioning

confidence: 99%

The Mono/Bistatic SAR Imaging Simulation of Sea Surface with Breaking Waves Based on a Refined Facet Scattering Field Model

Zhao

Guan

2021

International Journal of Antennas and Propagation

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In order to analyze the scattering characteristics of sea surface under high sea state, a complete scattering model of sea surface considering breaking wave is established in this study based on the refined facet scattering field model (RFSFM) and the scattering theory of breaking wave. On the basis of this model, the influence of breaking waves on the mono/bistatic SAR imaging of sea surface at HH and VV polarization is studied. The results show that with the increase in wind speed, the coverage of breaking wave increases obviously and the consideration of breaking wave has a good correction for the scattering coefficient at HH polarization under grazing incidence; however, for VV polarization, the effect of breaking wave is very small.

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Modeling Kelvin wake imaging mechanism of visible spectral remote sensing

Song

Wang

Zhao

et al. 2021

Applied Ocean Research

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Modeling and SAR Imaging of the Sea Surface: a Review of the State-of-the-Art with Simulations

Cited by 8 publications

References 128 publications

SynthWakeSAR: A Synthetic SAR Dataset for Deep Learning Classification of Ships at Sea

SynthWakeSAR: A Synthetic SAR Dataset for Deep Learning Classification of Ships at Sea

The Mono/Bistatic SAR Imaging Simulation of Sea Surface with Breaking Waves Based on a Refined Facet Scattering Field Model

Modeling Kelvin wake imaging mechanism of visible spectral remote sensing

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