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dc.contributor.authorVAN WIMERSMA GREIDANUS Hermanen_GB
dc.contributor.authorALVAREZ ALVAREZ Marleneen_GB
dc.contributor.authorSANTAMARIA SERNA CARLOSen_GB
dc.contributor.authorTHOORENS Francois-Xavieren_GB
dc.contributor.authorKOURTI Naoumaen_GB
dc.contributor.authorARGENTIERI PIETROen_GB
dc.date.accessioned2017-03-29T00:31:55Z-
dc.date.available2017-03-27en_GB
dc.date.available2017-03-29T00:31:55Z-
dc.date.created2017-03-08en_GB
dc.date.issued2017en_GB
dc.date.submitted2016-11-18en_GB
dc.identifier.citationREMOTE SENSING vol. 9 no. 3 p. 246en_GB
dc.identifier.issn2072-4292en_GB
dc.identifier.urihttp://www.mdpi.com/2072-4292/9/3/246en_GB
dc.identifier.urihttp://publications.jrc.ec.europa.eu/repository/handle/JRC104044-
dc.description.abstractSearch 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.en_GB
dc.description.sponsorshipJRC.E.7-Knowledge for Security and Migrationen_GB
dc.format.mediumOnlineen_GB
dc.languageENGen_GB
dc.publisherMDPI AGen_GB
dc.relation.ispartofseriesJRC104044en_GB
dc.titleThe SUMO Ship Detector Algorithm for Satellite Radar Imagesen_GB
dc.typeArticles in periodicals and booksen_GB
dc.identifier.doi10.3390/rs9030246en_GB
JRC Directorate:Space, Security and Migration

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