## 1 Introduction  
The recent expansion of the global economy has led to a significant increase in maritime traffic and offshore oil production, causing multiple negative consequences for marine life and impacting coastal activities and the people who rely on sea resources. According to [1], oil spills, in particular, have a profound effect on marine biodiversity, harming animals and plants in two main ways: through the toxic effects of the oil itself and the response or cleanup operations. Spilled oil is harmful because its chemical constituents are poisonous, affecting organisms internally through ingestion or inhalation and externally through skin and eye irritation. Additionally, oil can smother small species of fish or invertebrates and coat birds' feathers and mammals' fur, reducing their ability to maintain their body temperatures. Chemical toxins from oil spills can also devastate large areas of seagrass and coral reefs, which are crucial for marine biodiversity. [PERSON] et al. [2] conducted numerical and experimental studies to quantify the risk of oil spill impacts on biodiversity in the Tuscan Archipelago, a high oil-spill-density area in the Western Mediterranean. Their research highlighted a direct relationship between the temporary reduction in maritime traffic due to pandemic restrictions and the likelihood of oil spill damage in the archipelago. Consequently, detecting oil spills and monitoring oil drifts in near real-time contexts have become critical for many coastal nations. Oil spill detection has significantly improved in recent years, thanks to the increased use of remote sensing devices that can provide high-precision images. Among these technologies, Earth Observation (EO) satellites equipped with Synthetic Aperture Radar (SAR) and optical sensors are among the most widely used, due to their high spatial resolution and wide swath. In general, SAR can image sea surface roughness at small scale through the Bragg scattering resonance mechanism. As oil slicks appear on the sea surface, their viscosity dampens small-scale surface waves, and therefore the backscattering intensity captured by radars on the oil-contaminated sea surface is low (they are seen as dark objects) [3; 4; 5]. The primary challenge in SAR-based oil detection lies in distinguishing between actual oil and look-alikes, as both often exhibit similar radar backscatter or Normalized Radar Cross Section (NRCS), appearing as dark objects in different SAR images, including C-band Sentinel-1 [6], X-band Cosmo-SkyMed [7], and small SAR ICEYE-X [8], as well as polarimetric SAR [9]. The detection of oil spills in SAR images generally comprises segmentation, feature extraction, and classification procedures [10], in which the segmentation is the first step, employed to detect oil and look-alikes, while the discrimination between oil and look-alikes involves leveraging different features calculated from NRCS [11] and employing classification methods such as adaptive thresholding [12], machine learning (ML) [13], and deep learning (DL) [11; 14]. In order to cope with the speckle, the segmentation and detection of the dark areas are usually performed patch-wise, either using traditional methods like Markov-random field [15] or semi-empirical threshold modelling [16], or using DL approaches, e.g., YOLOv8 [17] ([PERSON] et al., 2024) and YOLOv4 [18] object detectors. A significant challenge with supervised approaches lies in the requirement to encompass a wide array of SAR oil spill scenes, including those obtained under challenging conditions in which look-alike examples are also present, in order to effectively train the model. To solve this issue, recent efforts have focused on augmenting the training data. One such approach proposed by [19] involves the development of a self-evolving deep learning model. Additionally, they utilized an adaptive thresholding method to generate supplementary high-quality training data.