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13 June 2023 Simulated SAR multi-vehicle scene dataset generation for deep learning-based surveillance systems
Ali Ahmadibeni, Branndon Jones, Amir Shirkhodaie
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Proceedings Volume 12529, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications; 125290E (2023) https://doi.org/10.1117/12.2664707
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States
Abstract
In this paper, we propose a comprehensive approach for the effective simulation of multi-object SAR imagery dataset generation using IRIS Electromagnetic modeling and simulation system – called IRIS-EM. Further, we describe our methodology for systematic generation of large-scale simulated SAR datasets of multi-domain (air, ground, sea) in multi-object scenes. Different from our earlier work, in this study, we considered the impact of having multiple objects in the same scene. We discuss the challenges associated with generating simulated SAR imagery datasets for multi-vehicle for the training of DL-based surveillance systems. Lastly, we present novel automation techniques ensuring realistic multi-vehicle placement in a test scene while sustaining real-world representation with fidelity.
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Ali Ahmadibeni, Branndon Jones, and Amir Shirkhodaie "Simulated SAR multi-vehicle scene dataset generation for deep learning-based surveillance systems", Proc. SPIE 12529, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications, 125290E (13 June 2023); https://doi.org/10.1117/12.2664707
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KEYWORDS
Synthetic aperture radar
Computer simulations
Device simulation
Surveillance systems
Scene simulation
Automatic target recognition
Data modeling
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