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Article

Mass Processing of Sentinel-1 Images for Maritime Surveillance

1
European Commission, Joint Research Centre (JRC), Directorate for Space, Security & Migration, Via E. Fermi 2749, I-21027 Ispra (VA), Italy
2
European Commission, Joint Research Centre (JRC), Directorate for Competences, Via E. Fermi 2749, I-21027 Ispra (VA), Italy
*
Author to whom correspondence should be addressed.
Remote Sens. 2017, 9(7), 678; https://doi.org/10.3390/rs9070678
Received: 24 May 2017 / Revised: 27 June 2017 / Accepted: 27 June 2017 / Published: 2 July 2017
(This article belongs to the Section Ocean Remote Sensing)
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. View Full-Text
Keywords: maritime surveillance; ship detection; Sentinel-1; big data; Synthetic Aperture Radar maritime surveillance; ship detection; Sentinel-1; big data; Synthetic Aperture Radar
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MDPI and ACS Style

Santamaria, C.; Alvarez, M.; Greidanus, H.; Syrris, V.; Soille, P.; Argentieri, P. Mass Processing of Sentinel-1 Images for Maritime Surveillance. Remote Sens. 2017, 9, 678. https://doi.org/10.3390/rs9070678

AMA Style

Santamaria C, Alvarez M, Greidanus H, Syrris V, Soille P, Argentieri P. Mass Processing of Sentinel-1 Images for Maritime Surveillance. Remote Sensing. 2017; 9(7):678. https://doi.org/10.3390/rs9070678

Chicago/Turabian Style

Santamaria, Carlos, Marlene Alvarez, Harm Greidanus, Vasileios Syrris, Pierre Soille, and Pietro Argentieri. 2017. "Mass Processing of Sentinel-1 Images for Maritime Surveillance" Remote Sensing 9, no. 7: 678. https://doi.org/10.3390/rs9070678

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