Basin-Wide Maritime Awareness From Multi-Source Ship Reporting Data

Greidanus, H.; Alvarez, Marlene; Eriksen, Torkild; Argentieri, P.; Cokacar, Tulay; Pesaresi, A.; Falchetti, Silvia; Nappo, Domenico; Mazzarella, Fabio; Alessandrini, Alfredo

doi:10.12716/1001.07.02.04

Cited by 14 publications

(14 citation statements)

References 9 publications

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“…However, verification of successful AIS detection via satellite is not so easy to do (Brusch et al, 2011; Zhi, 2014). In Greidanus et al (2013), a detection probability including satellite AIS and Long-Range Identification and Tracking (LRIT) were only 50% compared by radar survey. Within these evaluations, we take advantage of freely available global data from several global satellite AIS detection sets of ship patterns by the EU PASTA MARE Project (2010a).…”

Section: Ais Airliner Infrastructurementioning

confidence: 99%

Global Maritime Surveillance by Airliner-Based AIS Detection: Preliminary Analysis

et al. 2015

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Demands on security, safety, and environmental protection in worldwide shipping are steadily increasing. Shipboard broadcast transponders based on the Automatic Identification System (AIS) can be easily detected close to coastal or waterway areas. Satellite-based AIS receivers detect globally but are limited in high-density traffic areas. This paper investigates the challenges and performance of AIS detection on aircraft at altitudes between 8 500 m and 10 000 m. During flight trials over sea and land, AIS signals were recorded. Post-processing of the recorded data allows the evaluation but also faces challenges due to the nature of overlapping AIS signals at the aircraft. A comparison of detected signals at the aircraft with received AIS signals on the ground is given, including the evaluation of the reception footprint of the aircraft. Finally, a concept for worldwide AIS detection via airliners is presented. The study shows the potential for global complementary surveillance coverage via airliner-based AIS detection.

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Section: Ais Airliner Infrastructurementioning

confidence: 99%

Global Maritime Surveillance by Airliner-Based AIS Detection: Preliminary Analysis

et al. 2015

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“…The algorithms have been developed during a multi-year learning process based on the use of large amounts of real satellite SAR data. SUMO has been used in many scientific publications, in particular on fisheries' control [26][27][28][29][30][31], maritime surveillance [32][33][34][35][36] and benchmarking [37][38][39][40], but its detailed working has never been fully described.…”

Section: Introductionmentioning

confidence: 99%

The SUMO Ship Detector Algorithm for Satellite Radar Images

et al. 2017

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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.

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“…The Maritime Affairs Unit of the European Commission's Joint Research Centre (JRC) investigated the possibility of using AIS technology to provide a maritime domain awareness around the Horn of Africa and the Western Indian Ocean [1,12]. This action was a response to the piracy threat to maritime traffic in those areas, and the realization that the authorities in the region had no access to region-wide maritime awareness systems.…”

Section: Introductionmentioning

confidence: 99%

Using message brokering and data mediation on earth science data to enhance global maritime situational awareness

Delaney

Alessandrini

Greidanus

2016

IOP Conf. Ser.: Earth Environ. Sci.

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Abstract. Maritime Situational Awareness is the understanding of anything associated with the maritime domain that could impact the security, safety, economy, or environment. The European Commission's Joint Research Centre (JRC) has developed an in-house data collection, data analysis and data visualiztion facility, known as the Blue Hub. The Blue Hub operates as a research and development platform for integrated maritime surveillance and maritime situational awareness. It has global coverage and has been applied, for example, to support counter-piracy around Africa, to investigate fishing activity and to monitor the growing ship traffic in the Arctic. In order to improve maritime awareness and support risk assessment, the JRC has started to integrate data from the marine and atmosheric science community. In particular the JRC is interested in using forecasts from operational ocean models and weather models. For the Blue Hub a new type of data server, called ERDDAP, that performs message brokering and data mediation has become an essential tool for the accessing of ocean forecast data as quickly as possible in easy to use formats.NOAA (National Oceanic and Atmospheric Administration of the USA) is making global oceanography and

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Basin-Wide Maritime Awareness From Multi-Source Ship Reporting Data

Cited by 14 publications

References 9 publications

Global Maritime Surveillance by Airliner-Based AIS Detection: Preliminary Analysis

Global Maritime Surveillance by Airliner-Based AIS Detection: Preliminary Analysis

The SUMO Ship Detector Algorithm for Satellite Radar Images

Using message brokering and data mediation on earth science data to enhance global maritime situational awareness

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