Classification of ISAR ship imagery using transfer learning

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Inverse synthetic aperture radar (ISAR) is a common radar imaging technique used to characterize and classify non-cooperative targets. Traditional classification approaches use geometric features extracted from the images of known targets to form a training dataset that is later used to classify an unknown target. While these approaches work reasonably well, deep learning-based techniques have demonstrated significant improvements over conventional processing schemes in many areas of radar. However, the application of ISAR image classification is difficult when there are only small training datasets available. In this article, we address the small dataset problem by utilizing transfer learning. Different approaches are considered that can take advantage of the ship aspect angle to improve the overall stability and improve the final classification result. The new classification results are then compared with a traditional classification approach and a simple three-layer convolutional neural network. In addition, to better understand how the neural networks are working, saliency maps are used to visualize the trained network.

Original languageEnglish
Pages (from-to)25-36
Number of pages12
JournalIEEE Transactions on Aerospace and Electronic Systems
Volume60
Issue number1
Early online date24 Jul 2023
DOIs
Publication statusPublished - Feb 2024

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