TY - GEN
T1 - Comparison of feed forward neural networks and convolutional neural networks for SAR automatic target recognition
AU - Hunter, Heather
AU - Graber, Hans
N1 - Funding Information:
The authors would like to thank the U.S. Office of Naval Research for funding through grants N00014-14-1-0630.
Publisher Copyright:
© VDE VERLAG GMBH  Berlin  Offenbach.
PY - 2018
Y1 - 2018
N2 - Automatic target recognition (ATR) in synthetic aperture radar (SAR) images requires algorithms that are robust to the unique characteristics of a SAR image. As detection and classification can be time-consuming for even a SAR expert, neural networks (NNs) have emerged as a powerful solution to the problem. NNs learn by studying example images and can adapt to target and environmental variations in subsequent images. This work compares the accuracy of a Feed Forward Neural Network (FFNN) and a Convolutional Neural Network (CNN) on SAR images. While both networks achieved good performance, the CNN outperformed the FFNN.
AB - Automatic target recognition (ATR) in synthetic aperture radar (SAR) images requires algorithms that are robust to the unique characteristics of a SAR image. As detection and classification can be time-consuming for even a SAR expert, neural networks (NNs) have emerged as a powerful solution to the problem. NNs learn by studying example images and can adapt to target and environmental variations in subsequent images. This work compares the accuracy of a Feed Forward Neural Network (FFNN) and a Convolutional Neural Network (CNN) on SAR images. While both networks achieved good performance, the CNN outperformed the FFNN.
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M3 - Conference contribution
AN - SCOPUS:85050503336
T3 - Proceedings of the European Conference on Synthetic Aperture Radar, EUSAR
SP - 1022
EP - 1027
BT - EUSAR 2018 - 12th European Conference on Synthetic Aperture Radar, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th European Conference on Synthetic Aperture Radar, EUSAR 2018
Y2 - 4 June 2018 through 7 June 2018
ER -