CFAR-based automatic ship detection exploiting polarimetric correlation descriptors
Date Issued
September 19, 2025
DOI
10.1117/12.3070193
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.
File(s)![Thumbnail Image]()
Name
1381603 (2).pdf
Size
2.44 MB
Format
Adobe PDF
Checksum (MD5)
21694aaaf20207c72d84bf11d76f953e