# Mass Processing of Sentinel-1 Images for
## 3 Methods
### Ship Detection: SUMO  
SUMO is the JRC's tool for ship detection in satellite SAR images [26]. It was recently extensively described in [8], so here only a brief overview is given. SUMO accepts as input an image from any of several satellite SAR sensors--current (Sentinel-1, Radarsat-2, TerraSAR-X, Cosmo-SkyMed, and ALOS-2 PALSAR-2) or past (ERS-1, ERS-2, Radarsat-1, and ENVISAT ASAR)--carries out a Constant False Alarm Rate (CFAR) detection, and outputs the list of detected ships in an XML file. For the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Area of Interest** & **Mediterranean Sea** \\ \hline Study period & From 3 October 2014 to 30 September 2016 \\ Sensor & Sentinel-1A \\ Number of images & 11,647 \\ Excluding RFI & 11,573 \\ Image mode & IW \\ Product type & GRDH \\ Polarisation & 11,421 VV + VH \\  & 25 HH + HV \\  & 199 VV \\  & 2 VH \\ Image swath & \(\sim\)250 km \\ Resolution & \(\sim\)20 \(\times\) 22 m [range \(\times\) azimuth] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main details of the satellite SAR images used in this study.  
detection, SUMO assumes that the sea clutter conforms to a K distribution and locally estimates the parameters of this distribution in small non-overlapping tiles. Any pixel whose value is above a threshold derived from the local distribution is then detected. Groups of neighbouring detected pixels are agglomerated in detected targets or ships. SUMO carries out the pixel-based CFAR detection on each polarimetric channel independently and then merges the results in a single set of targets. Based on a target's attributes, a reliability level is assigned. Detections deemed to be azimuth ambiguities (based on the deterministic distance between the ambiguity and its originating object and on the object-ambiguity intensity ratio [27]) are automatically flagged and assigned the lowest reliability by SUMO. There is no attempt to flag range ambiguities. This is because the identification of range ambiguities is often a hard task: typical distance between a range ambiguity and its originating target is 100 km or more, while it is only around 5 km for azimuth ambiguities. This means that the source of the range ambiguity is, in many cases, outside the image extent, which prevents the flagging of the ambiguity by simple deterministic distance as in the case of azimuth ambiguities. SUMO can be operated in semi- or fully automated ways, depending on whether a human operator supervises and corrects the detections or not, respectively.  
In this study, the OpenStreetMap coastline [28] buffered by 250 m has been used to mask the land areas of the images. SUMO's operation has been fully automatic, i.e., the detections have not been verified by a human. Relatively high detection thresholds have been chosen, especially in the co-pol channels (HH and VV), since lower thresholds would produce a significant number of false alarms that are not eliminated by a human operator. Specifically, a nominal false alarm rate of \(10^{-7}\) and detection threshold adjustments of 10.0 and 2.0 for the co-pol and cross-pol channels, respectively, have been used. These adjustments regulate the CFAR detection threshold that is finally applied (see [8] for more details).