SAR Automatic Target Recognition Using Transfer Learning Approach

Maha Al Mufti, Esra Al Hadhrami, Bilal Taha, Naoufel Werghi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

22 Scopus citations

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%.

Original languageBritish English
Title of host publication2018 International Conference on Intelligent Autonomous Systems, ICoIAS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-4
Number of pages4
ISBN (Electronic)9781538663295
DOIs
StatePublished - 16 Oct 2018
Event2018 International Conference on Intelligent Autonomous Systems, ICoIAS 2018 - Singapore, Singapore
Duration: 1 Mar 20183 Mar 2018

Publication series

Name2018 International Conference on Intelligent Autonomous Systems, ICoIAS 2018

Conference

Conference2018 International Conference on Intelligent Autonomous Systems, ICoIAS 2018
Country/TerritorySingapore
CitySingapore
Period1/03/183/03/18

Keywords

  • automatic target recognition
  • component
  • deep learning
  • synthetic aperture radar

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