Deep learning model-based algorithm for SAR ATR

Robert D. Friedlander; Michael Levy; Elizabeth Sudkamp; Edmund Zelnio

doi:10.1117/12.2315265

9 May 2018 Deep learning model-based algorithm for SAR ATR

Robert D. Friedlander, Michael Levy, Elizabeth Sudkamp, Edmund Zelnio

Proceedings Volume 10647, Algorithms for Synthetic Aperture Radar Imagery XXV; 106470B (2018) https://doi.org/10.1117/12.2315265
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

Many computer-vision-related problems have successfully applied deep learning to improve the error rates with respect to classifying images. As opposed to optically based images, we have applied deep learning via a Siamese Neural Network (SNN) to classify synthetic aperture radar (SAR) images. This application of Automatic Target Recognition (ATR) utilizes an SNN made up of twin AlexNet-based Convolutional Neural Networks (CNNs). Using the processing power of GPUs, we trained the SNN with combinations of synthetic images on one twin and Moving and Stationary Target Automatic Recognition (MSTAR) measured images on a second twin. We trained the SNN with three target types (T-72, BMP2, and BTR-70) and have used a representative, synthetic model from each target to classify new SAR images. Even with a relatively small quantity of data (with respect to machine learning), we found that the SNN performed comparably to a CNN and had faster convergence. The results of processing showed the T-72s to be the easiest to identify, whereas the network sometimes mixed up the BMP2s and the BTR-70s. In addition we also incorporated two additional targets (M1 and M35) into the validation set. Without as much training (for example, one additional epoch) the SNN did not produce the same results as if all five targets had been trained over all the epochs. Nevertheless, an SNN represents a novel and beneficial approach to SAR ATR.

Citation Download Citation

Robert D. Friedlander, Michael Levy, Elizabeth Sudkamp, and Edmund Zelnio "Deep learning model-based algorithm for SAR ATR", Proc. SPIE 10647, Algorithms for Synthetic Aperture Radar Imagery XXV, 106470B (9 May 2018); https://doi.org/10.1117/12.2315265

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available