# Large-Scale Automatic Vessel Monitoring Based on Dual-Polarization Sentinel-1 and AIS Data
## 5 Conclusions  
In this study we exploit the Sentinel-1 dual-polarization characteristic for carrying out an operational, large-scale, automatic, reliable and accurate detection of vessels. The need to develop an operational detection technique for timely processing of large-scale datasets imposes important restrictions on the computational cost, thus giving the preference to lighten processing approaches. The newly developed detection algorithm relies on an adaptive threshold of the correlation between the co- and cross-polarization channels. This threshold is locally tuned by considering the SAR swath acquisition geometry, i.e., the range of incidence angles, in order to make the method applicable to an entire large size SAR image. The proposed methodology has been applied to different Sentinel-1 datasets and the results have been compared with corresponding AIS positions interpolated from AIS data flows. In addition, SAR detection results derived from the state-of-the-art CFAR algorithm applied to the separate dual polarization intensity channels, have been used in a comparative study. As demonstrated for two case studies focusing on the English Channel and the Pacific Mexican coastline, the high agreement of the results provided by the coherence-based method and the intensities-based detections indicates that SAR data are in general a reliable source of information for detecting vessels. The intersection of SAR detections set is higher than the number of AIS matched targets, thereby indicating the clear complementarity of SAR with respect to cooperative sources for maritime surveillance applications. Moreover, the number of coherence-based detections is lower than the one obtained with CFAR, thereby indicating that the rate of false alarms is reduced with the proposed methodology, as it has been investigated in this study by a visual inspection analysis. A performance assessment of the detection performances depending on the vessel size has also been presented, indicating that the detection rate is high for vessels of a size larger than 60 m, while for smaller sized vessels, in the case of the dual polarization coherence-based proposed method, it decreases from 80% to 40%. As a perspective for future work, we suggest to add a hierarchical dimension to the proposed dual-polarization based detector, by applying the following sequence of processing steps: (i) to parametrize the algorithm for large-sized vessels first, to detect and remove them from the initial SAR image, (ii) to apply the same procedure for smaller-sized vessels until the minimum size of detectable vessels given the SAR spatial resolution is reached. This will allow to improve the detection of small sized vessels while limiting the rate of false alarms. As noticed in the examples shown in this study, the signatures of the vessels appear differently in the dual polarization channels. This property is of interest for characterizing a vessel once it has been detected. For instance, in a future study we aim at deriving the vessel's kinematic characteristics from SAR SLC imagery by integrating the dual polarization information as well. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Case Study** & & **AIS \(\cap\) SAR** & **AIS with No SAR** & **SAR with No AIS** & **AIS \(\cup\) SAR (Vessel nb)** \\ \hline \(C_{VV-VH}^{un-norm}\) & \% wrt & 48\% & 18\% & 34\% & 2104 \\ CFAR \(I_{VH}\) & \(\mathbf{AIS\cup SAR}\) & 51\% & 12\% & 37\% & 2215 \\ CFAR \(I_{VV}\) & \(\mathbf{AIS\cup SAR}\) & 46\% & 17\% & 37\% & 2208 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: SAR detections, i.e., \(C_{VV-VH}^{un-norm}\), CFAR \(I_{VH}\) and CFAR \(I_{VV}\), and AIS positions global cross-comparison for the English Channel case study.