Pseudo-Zernike based multi-pass automatic target recognition from multi-channel SAR

The capability to exploit multiple sources of information is of fundamental importance in a battlefield scenario. Information obtained from different sources, and separated in space and time, provides the opportunity to exploit diversities to mitigate uncertainty. In this paper, we address the problem of Automatic Target Recognition (ATR) from Synthetic Aperture Radar (SAR) platforms. Our approach exploits both channel (e.g. polarization) and spatial diversity to obtain suitable information for such a critical task. In particular we use the pseudo-Zernike moments (pZm) to extract features representing commercial vehicles to perform target identification. The proposed approach exploits diversities and invariant properties of pZm leading to high confidence ATR, with limited computational complexity and data transfer requirements. The effectiveness of the proposed method is demonstrated using real data from the Gotcha dataset, in different operational configurations and data source availability.