Aerial and ground vehicles synthetic SAR dataset generation for automatic target recognition

Ali Ahmadibeni; Leila Borooshak; Branndon Jones; Amir Shirkhodaie

doi:10.1117/12.2558258

24 April 2020 Aerial and ground vehicles synthetic SAR dataset generation for automatic target recognition

Ali Ahmadibeni, Leila Borooshak, Branndon Jones, Amir Shirkhodaie

Proceedings Volume 11393, Algorithms for Synthetic Aperture Radar Imagery XXVII; 113930N (2020) https://doi.org/10.1117/12.2558258
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Synthetic Aperture Radar (SAR) technology offers innovative remote sensing opportunity for the area of surveillance applications. However, for the Automatic Target Recognition (ATR) of aerial and ground vehicles from SAR data, there is a need for large-scale imagery of the target objects of interest (TOI’s) from different perspective viewing angles – that is rarely available publically. Such large datasets can be very instrumental for the initial training of deep learning classifiers as well as for the achievement of improved transfer learning. In this paper, we address this shortcoming by introducing IRIS Electromagnetic (EM) modeling and simulation system for virtual staging and automatic generation of realistic synthetic (i.e. simulated) multi0perspective SAR imagery of the test vehicles for the purpose of training of ATR classifiers. Primarily, we prepared a collection of 250 physics-based CAD models containing different aerial and ground vehicles objects. A fourstep process was implemented. In the first step, an optimized multi-path ray-tracing technique was developed for obtaining the synthetic EM radiation backscattering reflectivity patterns of the test objects. In the second step, we furnish the synthetically generated SAR images with different backgrounds (e.g. ground, grass, and asphalt) by employing appropriate noise modulation transfer functions. In the third step, we introduced a method for projecting directional test objects’ shadows from eight different perspective viewings. In the final step, the surface regions producing high-strength radiation backscatterings were highlighted to further enhance realism of the synthetically generated SAR images. To test and verify the validity and dependability of this proposed approach, we compared our simulated SAR imagery results against a number of comparable military and commercial vehicles from MSTAR dataset.

Conference Presentation

Citation Download Citation

Ali Ahmadibeni, Leila Borooshak, Branndon Jones, and Amir Shirkhodaie "Aerial and ground vehicles synthetic SAR dataset generation for automatic target recognition", Proc. SPIE 11393, Algorithms for Synthetic Aperture Radar Imagery XXVII, 113930N (24 April 2020); https://doi.org/10.1117/12.2558258

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