Using transfer learning technique for SAR automatic target recognition

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

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

2 Scopus citations

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.

Original languageBritish English
Title of host publicationSPIE Future Sensing Technologies
EditorsMasafumi Kimata, Christopher R. Valenta
PublisherSPIE
ISBN (Electronic)9781510631113
DOIs
StatePublished - 2019
EventSPIE Future Sensing Technologies 2019 - Tokyo, Japan
Duration: 14 Nov 2019 → …

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11197
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceSPIE Future Sensing Technologies 2019
Country/TerritoryJapan
CityTokyo
Period14/11/19 → …

Keywords

  • Automatic Target Recognition
  • Deep learning
  • Remote sensing
  • Synthetic Aperture Radar
  • Transfer learning

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