Hierarchical classification of ISAR sequences

Luke Rosenberg*, Weiliang Zhao, Anthony Heng, Si Tran Nguyen, Len Hamey, Mehmet Orgun

*Corresponding author for this work

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

1 Citation (Scopus)


Inverse synthetic aperture radar (ISAR) is a common radar imaging technique used to characterise and classify non-cooperative targets. A number of different classification approaches have been proposed including the traditional approach which utilises geometric features extracted from images of known targets and more recently deep learning approaches. Hierarchical classification is designed to deal with large scale classification problems when there is a large number of targets. In this paper, a number of convolutional neural networks are compared to determine the best approach for hierarchical ship classification. Recent work on transfer learning, recurrent neural networks, and hierarchical structures are explored with the goal to find the best network design in terms of computational efficiency and classification accuracy.

Original languageEnglish
Title of host publication2023 IEEE International Radar Conference (RADAR)
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Electronic)9781665482783
ISBN (Print)9781665482790
Publication statusPublished - 2023
Event2023 IEEE International Radar Conference, RADAR 2023 - Sydney, Australia
Duration: 6 Nov 202310 Nov 2023


Conference2023 IEEE International Radar Conference, RADAR 2023


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