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Proceedings Paper

Convolutional neural networks for synthetic aperture radar classification
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Paper Abstract

For electro-optical object recognition, convolutional neural networks (CNNs) are the state-of-the-art. For large datasets, CNNs are able to learn meaningful features used for classification. However, their application to synthetic aperture radar (SAR) has been limited. In this work we experimented with various CNN architectures on the MSTAR SAR dataset. As the input to the CNN we used the magnitude and phase (2 channels) of the SAR imagery. We used the deep learning toolboxes CAFFE and Torch7. Our results show that we can achieve 93% accuracy on the MSTAR dataset using CNNs.

Paper Details

Date Published: 14 May 2016
PDF: 10 pages
Proc. SPIE 9843, Algorithms for Synthetic Aperture Radar Imagery XXIII, 98430M (14 May 2016); doi: 10.1117/12.2225934
Show Author Affiliations
Andrew Profeta, Wright State Univ. (United States)
Andres Rodriguez, Wright State Univ. (United States)
Intel Corp. (United States)
H. Scott Clouse, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 9843:
Algorithms for Synthetic Aperture Radar Imagery XXIII
Edmund Zelnio; Frederick D. Garber, Editor(s)

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