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Proceedings Paper

Using transfer learning technique for SAR automatic target recognition
Author(s): Maha Al Mufti; Esra Al Hadhrami; Bilal Taha; Naoufel Werghi
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Paper Abstract

In this paper, a deep learning approach for Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) is proposed. The novelty of the proposed framework stems from the fact that it is based on a transfer learning scheme, where a pre-trained Convolutional Neural Network (CNN) is employed to extract learned features in combination with a classical Support Vector Machine (SVM) for classification. The efficiency of the presented approach is validated on the MSTAR dataset, where ten target classes are used. A classification accuracy of 99.27% is achieved.

Paper Details

Date Published: 12 November 2019
PDF: 6 pages
Proc. SPIE 11197, SPIE Future Sensing Technologies, 111970A (12 November 2019); doi: 10.1117/12.2538012
Show Author Affiliations
Maha Al Mufti, Tawazun Technology and Innovation (United Arab Emirates)
Esra Al Hadhrami, Tawazun Technology and Innovation (United Arab Emirates)
Bilal Taha, Univ. of Toronto (Canada)
Naoufel Werghi, Khalifa Univ. of Science and Technology (United Arab Emirates)

Published in SPIE Proceedings Vol. 11197:
SPIE Future Sensing Technologies
Masafumi Kimata; Christopher R. Valenta, Editor(s)

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