# Large-Scale Automatic Vessel Monitoring Based on Dual-Polarization Sentinel-1 and AIS Data
## 1 Introduction  
Nowadays, over 80% of the world's trade is carried out by vessels navigating daily across the globe [1]. Therefore, the security and safety of maritime transportation is essential to ensure the international shipping of goods. At the same time, the maritime traffic is threatened by piracy attacks and other illegal activities such as drug trafficking, unreported and unregulated fishing or illegal immigration. In this context, maritime surveillance (MS), defined as the ability to monitor multiple sea activities, ranging from safe and secure transportation to illegal fishery, piracy or embargo breaches, permits us to create a comprehensive awareness of the maritime domain. MS is usually made possible by integrating information extracted from various data sources including space-basedsensors, land-based surveillance stations or in-situ observations. This information allows us to localize and track ships at a large scale in a timely manner. The MS data sources are generally divided in cooperative and non-cooperative systems. Examples of cooperative systems that rely on vessels to report their localization are the automatic identification system (AIS), the satellite AIS (Sat-AIS) or the vessel monitoring system (VMS). Non-cooperative systems that allow to localize vessels regardless of their agreement, include active and passive sensors onboard spaceborne or airborne platforms. Synthetic aperture radar (SAR) sensors occupy a privileged position in the non-cooperative systems due to their quasi all-weather and day/night observation capability. Ship detection applications based on SAR data have multiplied in the last decade, their importance being supported by the commercial sector, as mentioned in [2]. SAR imagery are widely used to monitor the maritime domain and various SAR-based applications such as ship detection or oil spill delineation are available as operational services integrated into near-real-time processing chains [3; 4; 5; 6]. Along with operational applications, a vast scientific literature has evaluated the potential of SAR imagery for detecting vessels [7; 8]. Generally, in SAR imagery, ships appear as bright points associated to high backscattering values while the surrounding smooth water surfaces (i.e., sea clutter) appear as dark areas characterized by low backscattering values. A variety of methods ranging from adaptive thresholding algorithms to wavelet- or machine learning-based approaches have been proposed in the literature in order to detect these bright targets. Whereas the majority of vessel detection methods exploit the SAR amplitude or intensity data, the SAR phase is commonly used for the retrieval of Doppler parameters corresponding to moving targets. Classical thresholding methods such as the constant false alarm rate (CFAR) detector, are derived with respect to the statistical distribution of the sea clutter. Depending on the SAR imaging mode and the equivalent number of looks, the SAR amplitude and intensity are characterized by different statistical distributions as summarized in [9]. For instance, the SAR amplitude is characterized by a Rayleigh distribution [10] while the intensity is assumed to be distributed according to a Gamma [11] or K [12] distribution. For SAR multi-looked images processed with a sufficiently high number of looks, the intensity follows a Gaussian distribution [13]. Therefore, one of the most common versions of the CFAR is based on the Gaussian distribution [14]. Other studies employ the Gamma [15; 16] or the K [5; 17] distribution for CFAR methods adapted to non-homogeneous areas. Other CFAR approaches exploit alpha-stable models [18], super-pixel topologies [19] or generalized Gamma distributions [20]. In addition, non-parametric models of the sea clutter, that allow us to fit real SAR data more precisely, are also used to compute the threshold for adaptive algorithms [21; 22]. Other ship detection methods rely on the use of the discrete wavelets transforms [23] or standard deviation-based filters [24].