## 1 Introduction  
Synthetic Aperture Radar (SAR) imagery is widely used in maritime surveillance, providing a robust and reliable solution for monitoring vast oceanic areas. Unlike optical sensors, which are limited by weather conditions and daylight, SAR systems actively emit microwave signals that penetrate clouds, fog, and precipitation, making them operational in all weather and lighting conditions. This capability is particularly critical for maritime surveillance, where oceans often experience dynamic and unpredictable environments. SAR images are used for a wide range of applications, including detecting and tracking ships, monitoring illegal activities such as smuggling and unauthorized fishing, and supporting search-and-rescue operations. The high-resolution imaging capabilities of SAR, combined with its ability to cover extensive areas, provide consistent and actionable insights, making it an indispensable asset for both civilian and defence-related maritime operations. A traditional and automatic ship detection method with SAR images is the one based on the well-known Constant False Alarm Rate (CFAR) and its variants [1; 2; 3; 4; 5; 6]. The fundamental idea of a CFAR detector consists in taking advantage of the intensity difference between the ship and the surrounding sea clutter. Since ships appear as bright targets in the SAR image, with a stronger radar response compared to sea clutter, CFAR methods aim to statistically model the background clutter (the sea/water) to effectively distinguish each ship from its surroundings. In other words, CFAR detectors assume that the statistical distribution of the sea clutter is known and use it to threshold the image to distinguish the ship from the background. Different statistical models have been proposed, including the Weibull-, Log-normal-, K-, or Gamma-distributed models [7; 8]. Major advantages of CFAR-based detectors are their fast processing speed and implementation simplicity. However, they are sometimes limited in complex scenarios where there is not enough contrast between the target (ship) and the background clutter, or for the detection of ships across multiple scales. Also, CFAR detection algorithms involve multiple processing parameters, the generalization of which is, normally, unfeasible. Such parameters are the dimensions of the search window and the probability of a false alarm, among others [4]. In recent years, with the inclusion of deep learning techniques for the exploitation of SAR data across a wide range of applications [9], sophisticated ship detection methods based on neural networks have emerged as a promising solution. A significant number of studies have demonstrated that advanced methods based on deep learning achieve similar or even higher detection accuracies compared to traditional algorithms. An extensive review of ship detection methods for SAR images based on deep learning can be found in the following references [10; 11; 12; 13; 14; 15]. An advantage of Artificial Intelligence (AI)-based detectors is their data-driven and non-parametric nature, meaning they do not require manual configuration of detection parameters. However, training these models effectively necessitates a large dataset to provide the neural network with enough information to learn meaningful features from target ships. Generating such datasets presents considerable difficulties because of the extensive number of SAR images that need to be processed and analysed (using conventional methods). Additionally, manual intervention is often required to ensure the dataset is accurately curated, further complicating the process of preparing reliable training data for neural network models. However, regardless of the detection method used, processing SAR images for automatically detecting ships poses significant challenges. This is mainly due to the following aspects. Firstly, the radar response of both ships and the sea surface is highly dependent on the sensor's imaging geometry.