Detection and recognition of 3D objects and their motion characteristics from Synthetic Aperture Radar (SAR) and Infrared (IR) Thermal imaging in the presence of significant clutter and occlusion is a highly challenging task. In this paper, we present an efficient technique for generation of static and dynamic synthetic SAR and IR imagery data in the cluttered virtual environments. Such imagery data sets closely represent the view of physical environment as potentially can be perceived by the physical SAR and IR imaging systems respectively. In this work, we present IRIS simulation model for the efficient construction and modeling of virtual environment with clutter and discuss our techniques for low-poly 3D object surface patch generation. Furthermore, we present several test scenarios based on which the synthetic SAR and IR imaging data sets are obtained and discuss the role of key control parameters impacting the performance of our synthetic multi-modality imaging systems. Lastly, we describe a method for multi-scale feature extraction from 3D objects based on synthetic SAR and IR imagery data sets for a variety of test ground-based and aerial-based vehicles and demonstrate efficiency and effectiveness of this approach in different test scenarios.
Amir Shirkhodaie, Yuanyan Zhou, and Leila Borooshak, "Multiscale synthetic SAR and IR imagery features generation in the cluttered virtual environment (Conference Presentation)," Proc. SPIE 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII, 1064619 (Presented at SPIE Defense + Security: April 18, 2018; Published: 5 October 2018); https://doi.org/10.1117/12.2305539.5783303757001.
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