Physics-based simulated SAR imagery generation of vehicles for deep learning applications

Branndon Jones; Ali Ahmadibeni; Amir Shirkhodaie

doi:10.1117/12.2568915

24 August 2020 Physics-based simulated SAR imagery generation of vehicles for deep learning applications

Branndon Jones, Ali Ahmadibeni, Amir Shirkhodaie

Proceedings Volume 11511, Applications of Machine Learning 2020; 115110T (2020) https://doi.org/10.1117/12.2568915
Event: SPIE Optical Engineering + Applications, 2020, Online Only

Abstract

This paper addresses large-scale multi-look dataset generation of multi-domain vehicles taken into account contributions of backscattered radiations from scatterers in the environment, operating contexts constraints, and atmospheric conditions. In this study, we use IRIS electromagnetic modeling and simulation system for virtualization of such test scenarios signifying the requirements. Through this approach, we initially construct and employ the physics-based CAD models of different category of vehicles in their inherent operating environments. Next, we apply physics-based remote sensing to generate realistic multimodality synthetic imagery of the test vehicles and systematically annotate them. To evaluate and verify the effectiveness of this approach, we compare the generated synthetic vehicle imagery with those images of the corresponding SAR remote sensors. In this paper, we discuss the technical aspects of this modeling and simulation and the obtained results.

Conference Presentation

Citation Download Citation

Branndon Jones, Ali Ahmadibeni, and Amir Shirkhodaie "Physics-based simulated SAR imagery generation of vehicles for deep learning applications", Proc. SPIE 11511, Applications of Machine Learning 2020, 115110T (24 August 2020); https://doi.org/10.1117/12.2568915

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