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High-performance computing strategies for SAR image experiments
Author(s): Michael A. Saville; David F. Short; Jeremy Trammell; John Kaufhold
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Paper Abstract

This article presents different strategies for generating very large sets of SAR phase history and imagery for target recognition studies using the open-use Raider Tracer simulation tool. Previous data domes, based on Visual D, produced numerous data sets for ground targets above a flat surface, but each target had a single orientation. Here, the experiment specifies different target types, each above a ground plane, but with arbitrary pose, yaw, and pitch. The customized data set poses challenges to load balancing and file input/output synchronization for a limited cpu hour budget. Strategies are presented to complete each image within a minimal time, and to generate the complete experiment set within a desired time.

Paper Details

Date Published: 27 April 2018
PDF: 10 pages
Proc. SPIE 10647, Algorithms for Synthetic Aperture Radar Imagery XXV, 1064703 (27 April 2018); doi: 10.1117/12.2299989
Show Author Affiliations
Michael A. Saville, Wright State Univ. (United States)
David F. Short, Wright State Univ. (United States)
Jeremy Trammell, Deep Learning Analytics, LLC (United States)
John Kaufhold, Deep Learning Analytics, LLC (United States)


Published in SPIE Proceedings Vol. 10647:
Algorithms for Synthetic Aperture Radar Imagery XXV
Edmund Zelnio; Frederick D. Garber, Editor(s)

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