CFAR-based automatic ship detection exploiting polarimetric correlation descriptors

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.