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Proceedings Paper

High-performance computing for automatic target recognition in synthetic aperture radar imagery
Author(s): Uttam Majumder; Erik Christiansen; Qing Wu; Nate Inkawhich; Erik Blasch; John Nehrbass
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Paper Abstract

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.

Paper Details

Date Published: 1 May 2017
PDF: 8 pages
Proc. SPIE 10185, Cyber Sensing 2017, 1018508 (1 May 2017); doi: 10.1117/12.2263218
Show Author Affiliations
Uttam Majumder, Air Force Research Lab. (United States)
Erik Christiansen, Univ. of Florida (United States)
Qing Wu, Air Force Research Lab. (United States)
Nate Inkawhich, Air Force Research Lab. (United States)
Erik Blasch, Air Force Research Lab. (United States)
John Nehrbass, Wright State Research Institute (United States)


Published in SPIE Proceedings Vol. 10185:
Cyber Sensing 2017
Igor V. Ternovskiy; Peter Chin, Editor(s)

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