@inproceedings{addb521db50a44549de2b33d3868c561,
title = "SAR Automatic Target Recognition Using Transfer Learning Approach",
abstract = "In this paper we propose a new approach for Synthetic Aperture Radar (SAR) automatic target recognition (ATR). One of the main obstacles in SAR ATR is the limited availability of datasets that are used for training. In this paper, a deep learning approach is employed for ATR. The proposed scheme is based on employing a pre-trained convolutional neural network (CNNs) as transfer learning. A pre-trained CNN namely AlexNet is utilized as a feature extractor whereas the output features are used to train a multiclass support vector machine (SVM) classifier. The effectiveness of the proposed framework is verified on a public database where the final result using three target classes attain an accuracy of 99.4%.",
keywords = "automatic target recognition, component, deep learning, synthetic aperture radar",
author = "{Al Mufti}, Maha and {Al Hadhrami}, Esra and Bilal Taha and Naoufel Werghi",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 International Conference on Intelligent Autonomous Systems, ICoIAS 2018 ; Conference date: 01-03-2018 Through 03-03-2018",
year = "2018",
month = oct,
day = "16",
doi = "10.1109/ICoIAS.2018.8494149",
language = "British English",
series = "2018 International Conference on Intelligent Autonomous Systems, ICoIAS 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--4",
booktitle = "2018 International Conference on Intelligent Autonomous Systems, ICoIAS 2018",
address = "United States",
}