# Large-Scale Automatic Vessel Monitoring Based on Dual-Polarization Sentinel-1 and AIS Data
## 3 Results
### English Channel Test Case
This analysis indicates that the proposed algorithm permitted us to reduce the false alarm rate which is a parameter of high importance for automatic detection chains employed in operational applications. For instance, having a high reliability for the detected ships while reducing the number of false positives allows to have a high confidence in the automatic detection performances and might reduce the human supervision of the results. Such detection chains were of interest for a systematic and automatic monitoring of the maritime traffic that does not require human intervention. Our approach  
Figure 4: Distribution of the AIS-SAR matching distances for the English Channel case study. provided a low number of false alarms and was robust and reliable for vessels of a size larger than the spatial resolution of the SAR image. However, if the monitoring applications were interested in vessels, with a size close to the spatial resolution of the SAR image, there was a risk not to detect them by the dual-polarization detector, triggering a number false negatives. The detection of such vessels, when possible, implies also a high number of false alarms and requires a visual inspection of the results with a certain cost for the operational applications. Figure 5 presents jointly the SAR \(C_{VV-VH}^{un-norm}\)-based detections and the AIS positions for the entire English Channel dataset. The vessel positions are extracted at different time instants corresponding to the acquisition time of the 10 Sentinel-1 SAR images. Despite the different acquisition times of the SAR images, the overlapping Sentinel-1 frames contained a sufficiently high number of targets to allow for a characterization of the maritime traffic in the English Channel area. We can observe that the dense traffic, characteristic to this area, was confirmed by both the SAR detections, as well as by the AIS positions. We can also notice that there was a strong agreement between the AIS and SAR positions for the majority of the zones. For instance, the main maritime routes characterizing the inward and outward-bound English Channel traffic, showed a strong AIS-SAR correlation. In order to analyze in more detail the AIS-SAR matching we have computed the SAR probability of detection by considering the AIS data as ground truth. Table 2 gives the number of targets resulting from the AIS-SAR matching. We can notice that more than 70% of the AIS positions have been matched with the SAR targets. The non-matched AIS targets were mostly due to the small size of the vessels (smaller than 20 m) that are hardly or not at all visible in the SAR images due to the limitations caused by the spatial resolution. When applying \(C_{VV-VH}^{un-norm}\)-based technique, the SAR and AIS matched targets represented about 60% of the total number of SAR detections. The nearly 40% SAR targets with no AIS positions correspond either to dark vessels or to false alarms generated by the sea clutter. A detailed discussion about the SAR unmatched targets resulting from \(C_{VV-VH}^{un-norm}\) as well as from the \(I_{VV}\) and \(I_{VH}\) images, will be provided in the next section. Figure 5: English Channel test case, vessel density: automatic identification system (AIS) positions (green squares) and synthetic aperture radar (SAR) \(C_{VV-VH}^{un-norm}\)-based detections (orange circles). The positions correspond to the cumulation of SAR detections for all the Sentinel-1 images of this dataset and their associated interpolated AIS positions. In the following we assess the statistics issued from the AIS-SAR matched targets. Figure 6a shows the distribution of the vessel length which was extracted from the AIS data flows. This information was of high importance in order to determine which vessel types can be detected from Sentinel-1 imagery. Figure 6b gives the probability of detection as a function of the AIS vessel length.