CFAR-based automatic ship detection exploiting polarimetric correlation descriptors
Date Issued
September 19, 2025
Abstract
Synthetic Aperture Radar (SAR) provides a reliable remote sensing solution for detecting ships over vast oceanic
regions under all-weather and day-night conditions. However, the normalized radar cross-section (NRCS) often
encounters ambiguities that hinder accurate detection. To address this, we utilized the polarimetric correlation
of SAR channels, including co-, cross-, and dual-polarization, to effectively mitigate or suppress these ambiguities
(or clutter). By integrating these polarimetric correlation descriptors with a constant false alarm rate (CFAR)
algorithm, we achieve automatic and robust ship detection. Experimental results demonstrate that the proposed
approach yields high detection accuracy with minimal root mean square error (RMSE), outperforming conventional manual image interpretation methods. The framework was applied to SAR data for the Mediterranean
Sea near Cyprus, showcasing its potential for detecting and monitoring ships, preventing illegal fishing, and
addressing maritime security challenges.
regions under all-weather and day-night conditions. However, the normalized radar cross-section (NRCS) often
encounters ambiguities that hinder accurate detection. To address this, we utilized the polarimetric correlation
of SAR channels, including co-, cross-, and dual-polarization, to effectively mitigate or suppress these ambiguities
(or clutter). By integrating these polarimetric correlation descriptors with a constant false alarm rate (CFAR)
algorithm, we achieve automatic and robust ship detection. Experimental results demonstrate that the proposed
approach yields high detection accuracy with minimal root mean square error (RMSE), outperforming conventional manual image interpretation methods. The framework was applied to SAR data for the Mediterranean
Sea near Cyprus, showcasing its potential for detecting and monitoring ships, preventing illegal fishing, and
addressing maritime security challenges.
Funding
The ‘EXCELSIOR’ project has received funding from the European Union’s Horizon 2020 research and innovation
programme under Grant Agreement No 857510, from the Government of the Republic of Cyprus through the
Directorate General for the European Programmes, Coordination and Development, and the Cyprus University of
Technology. The authors also acknowledge the ‘EXCELSIOR’: ERATOSTHENES: Excellence Research Centre
for Earth Surveillance and Space-Based Monitoring of the Environment H2020 Widespread Teaming project
(www.excelsior2020.eu), in which the Eratosthenes CoE has been established.
programme under Grant Agreement No 857510, from the Government of the Republic of Cyprus through the
Directorate General for the European Programmes, Coordination and Development, and the Cyprus University of
Technology. The authors also acknowledge the ‘EXCELSIOR’: ERATOSTHENES: Excellence Research Centre
for Earth Surveillance and Space-Based Monitoring of the Environment H2020 Widespread Teaming project
(www.excelsior2020.eu), in which the Eratosthenes CoE has been established.
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