A Hybrid Strategy Combining Maritime Physical Data to the OpenSARShip RCS Statistics for Fast and Effective Vessel Detection in SAR Imagery

Abstract

Highlights: What are the main findings? The Hybrid Strategy achieved comparable detection performance to pure CFAR–GΓD (Recall = 86.6%) while reducing execution time by ~18×. Integration of OpenSARShip backscatter statistics with environmental parameters (Wave Age) improved detection robustness and enabled preliminary vessel-size inference. What are the implications of the main findings? The Hybrid Strategy provides an efficient compromise between accuracy and computational cost, supporting scalable and near real-time vessel detection. The proposed thresholds allow autonomous monitoring of cooperative and non-cooperative vessels, strengthening maritime domain awareness. Maritime surveillance has become increasingly relevant due to the growth of shipping, illegal fishing, and the need to monitor remote oceanic regions. Synthetic Aperture Radar (SAR) imagery supports this task under day-and-night and almost all-weather conditions. However, automatic ship detection in heterogeneous ocean environments still faces challenges, especially regarding computational cost. This study develops and compares approaches for detecting vessels in SAR imagery using radar backscatter statistics ((Formula presented.)) to identify and characterize maritime targets. The OpenSARShip 2.0 dataset, which provides ship samples with AIS-based validation and reliable (Formula presented.) estimates by type and size, was combined with maritime physical parameters such as wave age (from ERA5 reanalysis). The objective is to combine fast processing, robustness to sea variability, and inference capability regarding target size for operational applications. Four algorithms were evaluated: Rapid Thresholding (RT), based on OpenSARShip (Formula presented.) values by ship length; Adjusted Rapid Thresholding (ART), with clutter-adapted thresholds; CFAR G (Formula presented.) D, based on Gamma pdf modeling of ocean clutter; and a Hybrid Strategy combining RT with CFAR G (Formula presented.) D. Results showed that CFAR G (Formula presented.) D achieved the highest recall (87.4%) but at high computational cost, while the Hybrid Strategy (HS) offered comparable performance (Recall: 86.6%; F1-score: 74.8%) with 18× faster execution time. RT and ART were faster but less sensitive. These findings highlight the HS as an efficient compromise, supporting scalable, near-real-time vessel detection systems.