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

Clemente, Carmine and Pallotta, Luca and Proudler, Ian and De Maio, Antonio and Soraghan, John J. and Farina, Alfonso (2015) Pseudo-Zernike based multi-pass automatic target recognition from multi-channel SAR. IET Radar Sonar and Navigation, 9 (4). 457–466. ISSN 1751-8784 (https://doi.org/10.1049/iet-rsn.2014.0296)

[thumbnail of Clemente-etal-RSN2014-pseudo-zernike-based-multi-pass-automatic-target]
Preview
 PDF. Filename: Clemente_etal_RSN2014_pseudo_zernike_based_multi_pass_automatic_target.pdf
Final Published Version
License: Creative Commons Attribution 3.0 logo
Download (1MB)| Preview

Abstract

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.

ORCID iDs

Clemente, Carmine ORCID logoORCID: https://orcid.org/0000-0002-6665-693X, Pallotta, Luca, Proudler, Ian, De Maio, Antonio, Soraghan, John J. ORCID logoORCID: https://orcid.org/0000-0003-4418-7391 and Farina, Alfonso;
CORE (COnnecting REpositories)