• A review of 119 papers on ship detection and classification from optical satellite. • From 1978 to March 2017, showing an exponential growth in the number of papers. • Most published methods have very limited validation. • While big steps have been made, automatic algorithms are still far from perfect. • Increase in new observation and processing capabilities promises rapid advances.

This paper provides a close investigation into the landscape of both cyber threats and actual incidents in the maritime sector, identifying the cyber trends and challenges as they relate to safe navigation and marine shipping. As an important subset of cyber threats that impact many maritime systems, the vulnerabilities of satellite navigation systems, in particular the Global Positioning System (GPS), receive special attention. For this article, a systematic literature review was conducted, complemented by the research and analysis of a specific spoofing event. Analyzing available resources, we might summarize that a shift in mind-set is essential to direct more attention and resources toward cybersecurity as well as the necessity for manufacturers to improve the cybersecurity of their products, as shipping systems currently remain vulnerable to cybercriminals. There is a need for multiple positioning, navigation, and timing (PNT) systems onboard maritime vessels to complement GPS-only navigation. The use of multiple satellite navigation constellations, public as well as private, in combination with the terrestrial components of an enhanced LOng-RAnge Navigation (eLoran) system and ports’ laser-based aid system for berthing and docking should provide the shipping industry with the direly needed increased protection from cyber-attackers for the foreseeable future.

Search for Unidentified Maritime Objects (SUMO) is an algorithm for ship detection in satellite Synthetic Aperture Radar (SAR) images. It has been developed over the course of more than 15 years, using a large amount of SAR images from almost all available SAR satellites operating in L-, C- and X-band. As validated by benchmark tests, it performs very well on a wide range of SAR image modes (from Spotlight to ScanSAR) and resolutions (from 1–100 m) and for all types and sizes of ships, within the physical limits imposed by the radar imaging. This paper describes, in detail, the algorithmic approach in all of the steps of the ship detection: land masking, clutter estimation, detection thresholding, target clustering, ship attribute estimation and false alarm suppression. SUMO is a pixel-based CFAR (Constant False Alarm Rate) detector for multi-look radar images. It assumes a K distribution for the sea clutter, corrected however for deviations of the actual sea clutter from this distribution, implementing a fast and robust method for the clutter background estimation. The clustering of detected pixels into targets (ships) uses several thresholds to deal with the typically irregular distribution of the radar backscatter over a ship. In a multi-polarization image, the different channels are fused. Azimuth ambiguities, a common source of false alarms in ship detection, are removed. A reliability indicator is computed for each target. In post-processing, using the results of a series of images, additional false alarms from recurrent (fixed) targets including range ambiguities are also removed. SUMO can run in semi-automatic mode, where an operator can verify each detected target. It can also run in fully automatic mode, where batches of over 10,000 images have successfully been processed in less than two hours. The number of satellite SAR systems keeps increasing, as does their application to maritime surveillance. The open data policy of the EU’s Copernicus program, which includes the Sentinel-1 satellite, has hugely increased the availability of SAR images. This paper aims to cater to the consequently expected wider demand for knowledge about SAR ship detectors.

The free, full and open data policy of the EU’s Copernicus programme has vastly increased the amount of remotely sensed data available to both operational and research activities. However, this huge amount of data calls for new ways of accessing and processing such “big data”. This paper focuses on the use of Copernicus’s Sentinel-1 radar satellite for maritime surveillance. It presents a study in which ship positions have been automatically extracted from more than 11,500 Sentinel-1A images collected over the Mediterranean Sea, and compared with ship position reports from the Automatic Identification System (AIS). These images account for almost all the Sentinel-1A acquisitions taken over the area during the two-year period from the start of the operational phase in October 2014 until September 2016. A number of tools and platforms developed at the European Commission’s Joint Research Centre (JRC) that have been used in the study are described in the paper. They are: (1) Search for Unidentified Maritime Objects (SUMO), a tool for ship detection in Synthetic Aperture Radar (SAR) images; (2) the JRC Earth Observation Data and Processing Platform (JEODPP), a platform for efficient storage and processing of large amounts of satellite images; and (3) Blue Hub, a maritime surveillance GIS and data fusion platform. The paper presents the methodology and results of the study, giving insights into the new maritime surveillance knowledge that can be gained by analysing such a large dataset, and the lessons learnt in terms of handling and processing the big dataset.

Automatic ship reporting systems (AIS – Automatic identification System, LRIT – Long Range Identification and Tracking, VMS – Vessel Monitoring System) today allow global tracking of ships. One way to display the results is in a map of current ship positions over an area of interest, the Maritime Situational Picture (MSP). The MSP is dynamic and must be created by fusing the reporting systems' messages, constructing ship tracks and predicting ship positions to correct for latency especially in the case of AIS received by satellite which forms the bulk of the data. This paper discusses the completeness of the resulting MSP and the accuracy of its positions, quantifying the additional value of the individual data sources.

Maritime awareness is a keystone of counter-piracy activities, as they are nowadays unfortunately called for in the Gulf of Aden and the Western Indian Ocean. There are a number of space-based systems that can be used to obtain knowledge of shipping and ship traffic patterns beyond coastal range, e.g. Satellite AIS, LRIT and satellite SAR. Based on data gathered during a trial in 2010, this paper analyses the capabilities of these systems when used to obtain a fused maritime picture. It is concluded that all data sources contribute to the maritime picture, but that in particular for Satellite AIS the update rates need to increase to enable accurate fusion of the non-cooperative data.