Convolutional Neural Networks-Based Ship Target Recognition Using High Resolution Range Profiles

In this paper, convolutional neural networks (CNN)-based ship target recognition is studied by exploiting the targets' high resolution range profiles (HRRPs). Contrary to conventional procedures employing hand-crafted features, by designing an appropriate CNN scheme, features are learned automatically in order through convolutional layers and, recognition of military and civilian ship targets is performed. In order to simulate the targets' scatterings accurately, their realistic computer-aided design (CAD) models are considered. Additionally, scattering characteristics of the targets are taken into account for a variety of azimuthal and elevation aspects. Promising simulation results exhibit that CNN-based schemes would provide easiness and enhanced performance in ship target recognition area due to their self-feature learning nature.