Deep convolutional neural networks for ATR from SAR imagery

David A. E. Morgan

doi:10.1117/12.2176558

13 May 2015 Deep convolutional neural networks for ATR from SAR imagery

David A. E. Morgan

Proceedings Volume 9475, Algorithms for Synthetic Aperture Radar Imagery XXII; 94750F (2015) https://doi.org/10.1117/12.2176558
Event: SPIE Defense + Security, 2015, Baltimore, MD, United States

Abstract

Deep architectures for classification and representation learning have recently attracted significant attention within academia and industry, with many impressive results across a diverse collection of problem sets. In this work we consider the specific application of Automatic Target Recognition (ATR) using Synthetic Aperture Radar (SAR) data from the MSTAR public release data set. The classification performance achieved using a Deep Convolutional Neural Network (CNN) on this data set was found to be competitive with existing methods considered to be state-of-the-art. Unlike most existing algorithms, this approach can learn discriminative feature sets directly from training data instead of requiring pre-specification or pre-selection by a human designer. We show how this property can be exploited to efficiently adapt an existing classifier to recognise a previously unseen target and discuss potential practical applications.

Citation Download Citation

David A. E. Morgan "Deep convolutional neural networks for ATR from SAR imagery", Proc. SPIE 9475, Algorithms for Synthetic Aperture Radar Imagery XXII, 94750F (13 May 2015); https://doi.org/10.1117/12.2176558

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