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dc.contributor.authorGroen, Johannes
dc.contributor.authorCoiras, Enrique
dc.contributor.authorWilliams, David
dc.date.accessioned2018-10-11T14:09:39Z
dc.date.available2018-10-11T14:09:39Z
dc.date.issued2009/12
dc.identifier36657
dc.identifier.govdocNURC-PR-2009-002
dc.identifier.urihttp://hdl.handle.net/20.500.12489/650
dc.description.abstractSynthetic aperture sonar (SAS) has proved to be successful for mine hunting andis now robust for generating high-resolution images over wide swath. The subsequent stepin the processing is detection, discriminating between mine-like and non-mine-like objects,which is designed to minimise the number of missed mines so that the system can managethe detection rate. Statistical analysis using SAS has been limited, because operational useof the technology is at an early stage. The design of automated detection and classificationsystems depends however on these statistics, which for a SAS are environment dependent.NURC has collected a comprehensive data set off the coast of Latvia with the MUSCLESAS, which comprises a wide range of seabeds, clutter and vehicle motion. The statisticalanalysis is based on 50 km2 of SAS images at centimetre resolution.
dc.format11 p. : ill. (digital, PDF file)
dc.languageEnglish
dc.publisherNURC
dc.sourceOriginally published in: Proceedings of the 3rd International Conference and Exhibition on Underwater Acoustic Measurements: Technologies and Results, 21-26 June, 2009, Nafplion, Greece.
dc.subjectSynthetic Aperture Sonar (SAS)
dc.subjectSonar images
dc.subjectImage processing
dc.subjectTarget detection
dc.subjectMine countermeasures (MCM)
dc.subjectNaval mines
dc.subjectMinehunting
dc.titleDetection rate statistics in synthetic aperture sonar images
dc.typeReprint (PR)
dc.typePapers and Articles


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