Classification of Reconstructed SAR Images Based on Convolutional Neural Network

Document Type : Original Article

Authors

1 Communications and Electronics Department Faculty of Electronic Engineering, Menoufia University Menouf, Egypt

2 Department of The Robotics and intelligent Mschines, Faculty of Artificial Intelligence, Kafrelsheikh University, Kafr El-Sheikh, Egypt

3 Electronics and Communications Engineering Department, Faculty of Engineering Minya University Minya, Egypt

4 Industrial Electronics andd Automatic Control Engineering Department Faculty of Electronic Engineering,Manoufia University: Menouf,Egypt

Abstract

Synthetic aperture radar (SAR) is a very important radar imaging type in which the utilization of antenna movement with respect to the target to be detected is considered. Detecting of target existence through noisy received images is a very critical and challenging point. Classification through deep learning presented in the form of Convolutional Neural Network (CNN) is a very good choice to enhance the decision performance reducing the error rate and false alarms. The main aim of this paper is to use a reliable classification technique in order to detect target existence through noisy received SAR images. Training data set for CNN is collected through a simulation in which realistic SAR images can be generated and used for SAR Automatic Target Recognition (ATR). CNNs are performed on images to classify the existence of targets. The accuracy of this approach is 100%.  

Keywords

References

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Menoufia Journal of Electronic Engineering Research
Volume 28, ICEEM2019-Special Issue
ICEEM2019-Special Issue: 1st International Conference on Electronic Eng., Faculty of Electronic Eng., Menouf, Egypt, 7-8 Dec.
2019
Pages 122-125

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