@inproceedings{ec62c2f452164e4697acd53e3f372d73,
title = "Automatic target recognition in SAR images: Comparison between pre-trained CNNs in a tranfer learning based approach",
abstract = "Synthetic aperture radar (SAR) are high resolution imaging radar systems. In many SAR applications classifying objects that are detected within the SAR image is important. In this paper an approach is proposed to tackle the Synthetic SAR Automatic Target Recognition (ATR) problem. The proposed scheme is based on a transfer leaning approach where three different pre-trained Convolutional Neural Networks (CNNs) are used as feature extractors in combination with a Support Vector Machine classifier (SVM). The CNNs used in this paper are AlexNet, VGG16 and GoogLeNet. The performance of these three CNNs is compared in regards to the SAR-ATR problem; where it is observed that AlexNet gives the best performance accuracy of 99.27\%.",
keywords = "Automatic target recognition, Deep learning, Synthetic aperture radar, Transfer learning",
author = "Mufti, \{Maha Al\} and Hadhrami, \{Esra Al\} and Bilal Taha and Naoufel Werghi",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 International Conference on Artificial Intelligence and Big Data, ICAIBD 2018 ; Conference date: 26-05-2018 Through 28-05-2018",
year = "2018",
month = jun,
day = "25",
doi = "10.1109/ICAIBD.2018.8396186",
language = "British English",
series = "2018 International Conference on Artificial Intelligence and Big Data, ICAIBD 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "160--164",
booktitle = "2018 International Conference on Artificial Intelligence and Big Data, ICAIBD 2018",
address = "United States",
}