# Large-Scale Automatic Vessel Monitoring Based on Dual-Polarization Sentinel-1 and AIS Data
## 3 Results
### English Channel Test Case  
In order to characterize the benefits of using a \(C_{VV-VH}^{un-norm}\)-based technique for ship detection, a statistical analysis with a large number of targets is required. To this end, the detector described in Section 2.2 has been applied to a set of 10 S1 IW images corresponding to the English Channel test case. As mentioned in the previous section, a conventional CFAR detector has been applied to the \(I_{VV}\) and \(I_{VH}\) intensity channels of the corresponding intensity data, as a benchmarking method. Figure 3a presents the qualitative detection results obtained by applying the \(C_{VV-VH}^{un-norm}\)-based and the CFAR \(I_{VH}\) detectors to a selected Sentinel-1 image. In order to simplify the understanding of the presented analysis, the detection results issued from CFAR \(I_{VV}\) are not shown in this example. The targets detected by both SAR methods represent vessels of sizes that range from small, composed of few pixels, to large, composed of many pixels with a clear signature. These targets were also characterized by backscattering values that are significantly higher than those of the sea clutter, as is illustrated in the examples shown in the right column of Figure 3b. We may also notice that the number of vessels detected by the \(C_{VV-VH}^{un-norm}\)-based detector is lower than for CFAR \(I_{VH}\). A visual analysis of the results indicates that the additional vessels detected by CFAR \(I_{VH}\) correspond to very small size objects (2 pixels) with low backscattering values, as it is illustrated for the signatures shown in the left column of Figure 3b. These targets can correspond to one of the following: (i) clutter-related false alarms and \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case Study** & **No. of Images** & **SLC Resolution [m]** & **Polarization** & **Sensing Dates** \\ \hline English Channel & 10 & 5 \(\times\) 20 (rg \(\times\) az) & VV-VH & October–November 2017 \\ Mexican marine areas & 20 & & October 2018 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of the Sentinel-1 synthetic aperture radar (SAR) datasets. Figure 2: Location of the Sentinel-1 frames for the two test case sites: (**a**) English Channel and (**b**) Mexico. (ii) small-size vessels with very low backscattering values in the VV channel and implicitly of \(C_{VV-VH}^{un-norm}\). As mentioned in the previous sections, a partial validation of these targets and of all SAR detection results can be performed by using AIS data. To this end, SAR detection results were cross-compared with AIS data flows in order to evaluate the SAR detection performances and also to assess the level of correlation and complementarity between non-cooperative and cooperative MS data sources. To this end, we have employed AIS vessel positions that were interpolated as described in Section 2.3. An iterative approach minimizing the distance between the SAR and AIS positions was employed in order to enable a meaningful comparison. The maximum tolerable distance between the SAR and AIS positions was two kilometres. Figure 4 gives the distribution of the AIS-SAR matching distance corresponding to the targets considered as matched for the English Channel case study. We can notice that half of the total number of targets were matched with a matching distance inferior to 500 m. We can notice that for distances ranging from 500 m to 2 km the number of AIS-SAR matched targets was decreasing. There were a considerable number of targets that were matched with a distance of about 1 km. This distance can be justified by the AIS interpolation inaccuracies due to missing information about the vessel speed. In addition, moving vessels were affected by a SAR Doppler shift that can also introduce inaccuracies in the AIS-SAR matching procedure, since the SAR targets were displaced from their original positions. For instance, vessels moving with about 15 knots can be displaced by about 700 m, resulting therefore in matching distances close to 1 km.