TY - GEN
T1 - Comparison of feed forward neural networks and convolutional neural networks for SAR automatic target recognition
AU - Hunter, Heather
AU - Graber, Hans
PY - 2018/1/1
Y1 - 2018/1/1
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.
UR - http://www.scopus.com/inward/record.url?scp=85050503336&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050503336&partnerID=8YFLogxK
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 -