# Large-Scale Automatic Vessel Monitoring Based on Dual-Polarization Sentinel-1 and AIS Data
## 1 Introduction
Alternatively, machine learning based approaches are also able to efficiently detect ships from SAR imagery. Such techniques include deep neural networks [25], graph signal processing [26], artificial neural networks (ANN) and support vector machine (SVM) [27] methods. In addition to the above mentioned research studies, highly relevant research questions emerged from the recent advances in SAR imaging capabilities, and new satellite-based MS services are triggered by the higher time sampling rates provided by new satellite constellations. A prime example is the Sentinel-1 SAR mission [28] which has as main assets: an open access data policy, three to six days systematic revisit time depending on the geographical region and multiple acquisition modes. Additional and highly complementary assets for MS derive from other SAR sensors such as the COSMOSkyMed (CSK) [29] or the TerraSAR-X and TanDEM-X [30] satellite constellations, providing images with spatial resolutions down to one meter and a revisit time of 12 hours. These characteristics allow us to advance MS by unlocking the full potential of satellite earth observation (EO) for large scale, systematic and rapid vessel detection. One key characteristic of Sentinel-1 images deserving attention for vessel detection is the availability of dual polarization data. The dual polarized antenna allows transmitting one single, but selectable, polarization (H or V) and receiving simultaneously both H and V polarizations. The Sentinel-1 images over areas of maritime interest are primarily acquired in the VV-VH polarimetric configuration. Vessels imaged by such sensors, present distinctive polarization signatures, i.e., different backscattering values depending on the polarization channel. The complex Sentinel-1 images can lead to different polarimetric parameters that are of interest for SAR-based ship detection. For instance, in [31] the polarimetric Notch filter [32] is adapted for the dual-polarization case which is well suited for processing the data generated by Sentinel-1. Other studies [33; 34], apply a thresholding operation on the coherence between co- and cross-polarization channels in order to detect the vessels. In addition, the polarimetric diversity has demonstrated its effectiveness in ship detection through several algorithms based on full (quad) polarimetric SAR data [35; 36; 37]. Such algorithms allow us to detect vessels in complex clutter scenarios, while reducing the number of false alarms with respect to single polarization based algorithms. The high revisit time of Sentinel-1 provides access to large collections of SAR data. Large datasets are necessary to perform a comprehensive analysis of SAR-based detection capabilities and performances. Such an analysis is performed in [22; 38] by using SAR images acquired by different sensors including Sentinel-1, but also Radarsat-2 and CSK, together with AIS data flows as ground truth. A large number of Sentinel-1 images (more than 10,000) is employed in [39] for performing a SAR-AIS cross-comparison study over the Mediterranean sea. Another study that makes use of the large Sentinel-1 dataset in order to create a benchmarking SAR-AIS dataset is presented in [40]. In this paper, we address two main research objectives: (i) to develop a new detection algorithm based on the dual polarimetric coherence and (ii) to evaluate the detection performances as a function of the vessels size, at a large scale. Therefore, the developed method exploits the particular characteristics of Sentinel-1, namely the dual polarization capability and the systematic acquisition of images. First, we propose to use the polarimetric complex coherence between the co- and cross- channel, instead of the intensity, to perform vessel detection. Although the capability to detect vessels in the dual polarization coherence domain has been demonstrated in previous studies [34; 41], this feature has not been used so far in automatic detection chains, processing large datasets systematically and in a timely manner.