# Mass Processing of Sentinel-1 Images for
## 5 Conclusions  
The free, full, and open nature of the Sentinel-1 SAR images gives access to unprecedented volumes of data. This presents opportunities, but these will not come to fruition unless the challenges that also come with the huge data volume are confronted. This paper has presented tools and platforms developed at the JRC to deal with these challenges, and the results that can be achieved for maritime surveillance. Two years' worth of Sentinel-1 imagery of the Mediterranean Sea (around 11,500 products) have been automatically analysed with SUMO (a ship detection tool) on the JEODPP platform with a processing time of <2 h. The versatility of the JEODPP platform was key to the success of this analysis. Indeed, the JEODPP enables complex and consolidated scientific workflows such as SUMO developed by domain experts over the years to be deployed at scale without embarking into the code rewriting, an operation that can prove very lengthy and challenging without guarantee of success. The ship detections (more than 600,000) have then been fused with ship reporting data (AIS) on the Blue Hub maritime surveillance platform.  
The automatic analysis of such a large set of images taken under diverse weather conditions means that certain steps have to be taken to prevent the detection of too many false alarms. Besides the exclusion of images with too much RFI, the most important adaptation is raising the detection thresholds in particular in the co-pol channels. Consequently, small boats are not detected. Further work would be needed to better discard false alarms fully automatically at lower detection thresholds.  
On the other hand, the availability of repeat observations allows for the identification of recurrent detections, most of which are false alarms or otherwise of no interest for maritime surveillance purposes. The high presence of these recurrent detections (about 20% of all the reliable detections) indicates the value added by this analysis, especially in coastal areas. In fact, the recurrent targets analysis teaches us that when analysing a SAR image on an individual basis, a significant fraction of the detections may be ambiguities, which would never be recognised because the ambiguity source  
Figure 13: Close up sections: (**a**) South of Sicily; and (**b**) around Sardinia of the density map of non-recurrent ship detections (Figure 12).  
is not inside the image or remains unnoticed. In this study, over the Med Sea, the fraction is 5 % on average and higher than that in areas near urbanised coasts.  
The results show that the big dataset of Sentinel-1 images can be used to map shipping activity, revealing the main shipping lanes, ports, anchor areas, off-shore supply and fishing activities. Even if limited to the detection of medium and large ships, comparison with AIS data shows the presence of many ships for which no position reports are available.  
Contains modified Copernicus Sentinel data [2014-2017]. Satellite AIS data from AISSat-1, AISSat-2 and NORAIS were obtained courtesy of the Norwegian Coastal Administration and the Norwegian Defence Research Establishment (FFI). MSSIS data were obtained courtesy of the Volpe Center of the US Department of Transportation, the US Navy and SPAWAR. Terrestrial AIS from an Italian network of coastal stations was obtained courtesy of the Italian Coast Guard. Land masking in SUMO was done using coastline data derived from OpenStreetMap, \" OpenStreetMap contributors.  
[PERSON], [PERSON] and [PERSON] designed the study and were responsible for SUMO and Blue Hub. [PERSON] and [PERSON] were responsible for the deployment of SUMO on the JEODPP. [PERSON] adapted SUMO to run on a Linux operating system. [PERSON] carried out the scientific analysis and prepared the manuscript, with contributions from [PERSON] and [PERSON] for the JEODPP sections.  
The authors declare no conflict of interest.