Many research efforts have been devoted to applying machine learning (ML) algorithms to the task of Automatic Target Recognition (ATR). In the 90’s, ML techniques such as Neural Networks were less popular due to various technological barriers and applications. Computational resources were scarce and expensive. Today, computational resources are not as expensive as in the past; however, an abundance of sensors and business data need to be analyzed in real-time. High performance computing (HPC) enables ML-based decision making in real-time or near real-time. This research explores the application of deep learning algorithms, specifically convolutional neural networks, to the task of ATR in synthetic aperture radar (SAR) imagery. We developed a Convolution Neural Networks (CNN) architecture for achieving ATR in SAR imagery and found that classification accuracy levels of 99% can be achieved through the application of neural networks. We used graphics processing units (GPU) to accomplish the computational tasks.
Uttam Majumder, Erik Christiansen, Qing Wu, Nate Inkawhich, Erik Blasch, and John Nehrbass, "High-performance computing for automatic target recognition in synthetic aperture radar imagery," Proc. SPIE 10185, Cyber Sensing 2017, 1018508 (Presented at SPIE Defense + Security: April 11, 2017; Published: 1 May 2017); https://doi.org/10.1117/12.2263218.
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