ISAR Ship Classification Using Metadata Features
Luke Rosenberg, Weiliang Zhao, Anthony Heng, Nabaraj Dahal, Len Hamey, Mehmet Ali Orgun · 2022
Inverse synthetic aperture radar (ISAR) is a common radar imaging technique used to characterise and classify non-cooperative targets. Different approaches to classification have been proposed and include the traditional approach using geometric features extracted from images of known targets and more recently, deep learning approaches that utilise transfer learning to deal with the small training datasets typically available. However, the challenge in a real-world scenario will be when no target training data is available and a different approach to classification will be required. In this work, we develop a deep neural network-based approach by utilising metadata features to enhance the performance of ISAR ship classification and provide an alternative metadata-only solution for ISAR ship classification.