# Large-Scale Automatic Vessel Monitoring Based on Dual-Polarization Sentinel-1 and AIS Data
## 3 Results  
### Sentinel-1 and AIS Datasets, Benchmarking Methods  
For the experimental results, we analyze two case studies making use of two different AIS-SAR datasets acquired over the English Channel and the Pacific Mexican marine areas. The SAR dataset is composed of Sentinel-1 images in the interferometric wide swath (IW) mode, which is the main Sentinel-1 mode to acquire data in areas of MS interest. Table 1 summarizes the characteristics of the Sentinel-1 images for both case studies. Single Look Complex (SLC) products with a spatial resolution rg \(\times\) az of approximately 5 \(\times\) 20 m have been considered in order to assess the ship signatures at the sensor's full resolution. The corresponding polarization channels are VV and VH. First, the dual-polarization algorithm described in Section 2.2 was applied to the Sentinel-1 datasets in order to carry out the SAR-based ship detection. In order to estimate the values of \(C_{VV-VH}^{un-norm}\) we have employed a sliding window with a size of \(k\times k\) pixels, testing different values for \(k\) ranging from 3 to 15 pixels. Based on a visual analysis of several targets of different sizes we found that the optimal value allowing to preserve small size targets while reducing the noise at the given pixel size of Sentinel-1 IW SLC images, was 5 pixels and was further utilized in this study. In addition to the \(C_{VV-VH}^{un-norm}\)-based detector, we also make use of the constant false alarm rate (CFAR) detector as a state-of-the-art method for performing the ship detection from VV and VH Sentinel-1 intensity images, \(I_{VV}\) and \(I_{VH}\). The CFAR applied to intensity images is widely used in operational chains and was used as benchmarking method for our proposed dual-polarization based algorithm that aims in a similar way to process SAR images systematically and rapidly. The detector employed to this end, was a cell-averaging CFAR based on the Gaussian distribution. As a brief reminder, the CFAR algorithm compares pixels from a target cell within a sliding window, i.e., pixels under test \(p_{test}\), to a threshold depending on the statistics of the surrounding area, i.e., background cell \(p_{background}\). The threshold values are determined by considering the statistics in the background cell and the corresponding probability density function (PDF) for a desired probability of false alarm (PFA) defined as follows:  
\[PFA=\int_{\tau}^{\infty}f(x)dx, \tag{5}\]  
where \(x\) ranges within the possible values of \(p_{test}\). Once the values of \(\tau\) are estimated by considering the Gaussian distribution and a given PFA, the detector is defined as a two-parameter CFAR:  
\[p_{test}>\mu_{background}+\sigma_{background}\tau, \tag{6}\]  
where, \(\mu_{background}\) and \(\sigma_{background}\) represent the statistical parameters of the background area, mean and respectively standard deviation. In order to respect the Gaussian distribution assumption, the VV and VH intensity image are multi-looked considering 4 \(\times\) 1 looks (rg \(\times\) az). For the Sentinel-1 IW images this resulted in a spatial resolution of 20 m in both range and azimuth directions. For the experimental results presented in this study we have set the PFA value to \(10^{-5}\) for both VV and VH intensity channels. The two benchmarking methods are denoted in the following as CFAR \(I_{VV}\) and \(I_{VH}\). The AIS positions were extracted from AIS dataflows gathered from terrestrial AIS base station networks, as well as from satellite AIS (SAT-AIS) systems. The access to the AIS data has been provided by LuxSpace, which is the Luxembourgish industrial partner in the project that supports this study. We have compiled two AIS datasets in order to analyze the two different test cases. The first one focused on the English Channel area characterized by a very dense maritime traffic. Figure 2a depicts the frames of the Sentinel-1 images used for this case study. The corresponding AIS data are extracted within a time interval of 30 min (X = 15 min).