Simulated SAR imagery generation of marine vehicles and associated wakes using electromagnetic modeling and simulation techniques

Branndon Jones; Ali Ahmadibeni; Amir Shirkhodaie

doi:10.1117/12.2600500

1 August 2021 Simulated SAR imagery generation of marine vehicles and associated wakes using electromagnetic modeling and simulation techniques

Branndon Jones, Ali Ahmadibeni, Amir Shirkhodaie

Author Affiliations +

Proceedings Volume 11843, Applications of Machine Learning 2021; 118430B (2021) https://doi.org/10.1117/12.2600500
Event: SPIE Optical Engineering + Applications, 2021, San Diego, California, United States

Abstract

Aerial automatic detection, tracking, and classification of small, fast moving marine objects in Synthetic Aperture Radar (SAR) data is a real challenge. Particularly, for many marine security applications, it is imperative to have automatic capability for identification of anomalous patterns of behaviors of target marine vehicles from their ocean wakes. By applying analytics on patterns of cruising of adversarial marine targets certain anomalous behavioral patterns can be identified and perilous situations can be mitigated. For learning such complex ocean wake patterns, deep learning classifiers can be trained. However, they need rich training datasets that are rarely publicly available. To overcome this shortcoming, there is a need for a large-scale multi-look (i.e., different perspective views) dataset of multi-vehicle TOI’s from different stand-off ranges, and diverse operating and environmental contexts including atmospheric conditions. In this study, we used IRIS electromagnetic modeling and simulation (IRIS-EM) virtual environment system to systematically generate a large-scale synthetic SAR imagery dataset of the test marine scenarios. A typical marine scenario includes physics-based CAD models of test marine vehicle(s) and their associated wake(s) as well as the ocean layer. In this paper, we present our systematic approach for generating synthetic SAR Imagery of marine test scenarios and detail our methodology for annotating the generated imagery methodically. To evaluate and verify the effectiveness of this approach, we bench-marked our generated simulated marine SAR imagery with similar context images taken by the physical SAR imaging systems.

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Branndon Jones, Ali Ahmadibeni, and Amir Shirkhodaie "Simulated SAR imagery generation of marine vehicles and associated wakes using electromagnetic modeling and simulation techniques", Proc. SPIE 11843, Applications of Machine Learning 2021, 118430B (1 August 2021); https://doi.org/10.1117/12.2600500

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KEYWORDS

Synthetic aperture radar

Ocean optics

Reflectivity

Device simulation

Coastal modeling

Image processing

Solid modeling

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