Convolutional neural networks for synthetic aperture radar classification

Andrew Profeta; Andres Rodriguez; H. Scott Clouse

doi:10.1117/12.2225934

14 May 2016 Convolutional neural networks for synthetic aperture radar classification

Andrew Profeta, Andres Rodriguez, H. Scott Clouse

Proceedings Volume 9843, Algorithms for Synthetic Aperture Radar Imagery XXIII; 98430M (2016) https://doi.org/10.1117/12.2225934
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

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.

Citation Download Citation

Andrew Profeta, Andres Rodriguez, and H. Scott Clouse "Convolutional neural networks for synthetic aperture radar classification", Proc. SPIE 9843, Algorithms for Synthetic Aperture Radar Imagery XXIII, 98430M (14 May 2016); https://doi.org/10.1117/12.2225934

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