Synthetic Aperture Radar (SAR) is a mature remote sensing technology which can form high-resolution images of the earth’s surface. This project seeks to examine advanced SAR-based surface and underwater object detection and classification. Underwater objects cannot be directly detected via SAR since electromagnetic radiation is reflected and absorbed by the ocean, however SAR can detect surface currents and ocean swell to indirectly perform bathymetry. These techniques therefore potentially provide an avenue for detecting an underwater object. The detection and classification of surface vessels can also be potentially improved by exploiting ocean phenomenon, such as wakes, in addition to using advanced beamforming approaches. This PhD project seeks to explore these main lines of effort before narrowing the scope in the initial stages of the project.

The proliferation of surface and underwater vessels in the Asia Pacific region is a concern to the Australian Government [1]. Underwater objects are challenging to detect, particularly on a global scale. The detection of underwater objects via remote sensing would undoubtedly be beneficial, particularly using Synthetic Aperture Radar (SAR), which is a day-and-night all-weather remote sensing technology. Increases in global shipping has also increased the number of different types of ships navigating the world’s oceans. Detection and classification of these vessels is an important problem for governments around the world, particularly for addressing piracy and illegal fishing. This project seeks to explore advanced detection and classification of surface and sub-surfaces vessels using imaging radar phenomenologies and exploitation approaches.