Comparison of feed forward neural networks and convolutional neural networks for SAR automatic target recognition

Heather Hunter, Hans Graber

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationEUSAR 2018 - 12th European Conference on Synthetic Aperture Radar, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1022-1027
Number of pages6
ISBN (Electronic)9783800746361
StatePublished - Jan 1 2018
Event12th European Conference on Synthetic Aperture Radar, EUSAR 2018 - Aachen, Germany
Duration: Jun 4 2018Jun 7 2018

Publication series

NameProceedings of the European Conference on Synthetic Aperture Radar, EUSAR
Volume2018-June
ISSN (Print)2197-4403

Other

Other12th European Conference on Synthetic Aperture Radar, EUSAR 2018
CountryGermany
CityAachen
Period6/4/186/7/18

ASJC Scopus subject areas

  • Signal Processing
  • Instrumentation

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  • Cite this

    Hunter, H., & Graber, H. (2018). Comparison of feed forward neural networks and convolutional neural networks for SAR automatic target recognition. In EUSAR 2018 - 12th European Conference on Synthetic Aperture Radar, Proceedings (pp. 1022-1027). (Proceedings of the European Conference on Synthetic Aperture Radar, EUSAR; Vol. 2018-June). Institute of Electrical and Electronics Engineers Inc..