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Proceedings Paper

A SAR dataset for ATR development: the Synthetic and Measured Paired Labeled Experiment (SAMPLE)
Author(s): Benjamin Lewis; Theresa Scarnati; Elizabeth Sudkamp; John Nehrbass; Stephen Rosencrantz; Edmund Zelnio
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Paper Abstract

The publicly-available Moving and Stationary Target Acquisition and Recognition (MSTAR) synthetic aperture radar (SAR) dataset has been an valuable tool in the development of SAR automatic target recognition (ATR) algorithms over the past two decades, leading to the achievement of excellent target classification results. However, because of the large number of possible sensor parameters, target configurations and environmental conditions, the SAR operating condition (OC) space is vast. This leads to the impossible task of collecting sufficient measured data to cover the entire OC space. Thus, synthetic data must be generated to augment measured datasets. The study of synthetic data fidelity with respect to classification tasks is a non-trivial task. To that end, we introduce the Synthetic and Measured Paired and Labeled Experiment (SAMPLE) dataset, which consists of SAR imagery from the MSTAR dataset and well-matched synthetic data. By matching target configurations and sensor parameters among the measured and synthetic data, the SAMPLE dataset is ideal for investigating the differences between measured and synthetic SAR imagery. In addition to the dataset, we propose four experimental designs challenging researchers to investigate the best ways to classify targets in measured SAR imagery given synthetic SAR training imagery.

Paper Details

Date Published: 14 May 2019
PDF: 16 pages
Proc. SPIE 10987, Algorithms for Synthetic Aperture Radar Imagery XXVI, 109870H (14 May 2019); doi: 10.1117/12.2523460
Show Author Affiliations
Benjamin Lewis, Air Force Research Lab. (United States)
Theresa Scarnati, Air Force Research Lab. (United States)
Elizabeth Sudkamp, Air Force Research Lab. (United States)
John Nehrbass, Wright State Univ. (United States)
Stephen Rosencrantz, Wright State Univ. (United States)
Edmund Zelnio, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 10987:
Algorithms for Synthetic Aperture Radar Imagery XXVI
Edmund Zelnio; Frederick D. Garber, Editor(s)

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