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dc.contributor.advisorBrekke, Camilla
dc.contributor.authorTao, Ding
dc.date.accessioned2016-05-27T11:54:16Z
dc.date.available2016-05-27T11:54:16Z
dc.date.issued2015-12-03
dc.description.abstractThis thesis discusses the subject of maritime target detection based on single- and multi-polarization synthetic aperture radar (SAR) data. The primary objective is to develop an automatic and effective target detection algorithm, which is able to provide robust performance for an operational maritime surveillance system under various circumstances. There are two frequently encountered major detection issues in practice. Firstly, in multiple-target situations, the local reference sea clutter is often contaminated by interfering targets. The outcome is known as the capture effect. Secondly, in non-homogeneous environments, sea surface transitions between regions with different radar backscattering properties are usually observed in conjunction with various meteorological and oceanographic phenomena. The result is recognized as the clutter edge effect. Both effects inevitably lead to inaccurate parameter estimation and deceptive statistical modeling, thus causing severe degradation of the detection performance. In this study, the conventional constant false alarm rate (CFAR) detector setup is adopted in the algorithm. The rigorous statistical analysis using truncated statistics (TS) provides improved background clutter modeling results, while a corresponding TS-based CFAR (TS-CFAR) detector is first proposed to suppress the capture effect and ameliorate detection performance. Moreover, an automatic image segmentation stage with suitable statistics is then adopted in the detection scheme to address the clutter edge issue simultaneously. It provides a comprehensive statistical analysis of the non-homogeneous background clutter and obtains the local contextual information for the subsequent CFAR detection stage. The end product is a segmentation based CFAR detection algorithm using TS. Single-look intensity (SLI) and multi-look intensity (MLI) SAR imagery are mainly targeted in this study for directly supporting operational applications. It has been demonstrated on several real SAR images, that the proposed algorithm is able to adapt to various contaminated non-homogeneous environments, provide improved local background clutter modeling, and deliver robust detection performance.en_US
dc.description.doctoraltypeph.d.en_US
dc.description.popularabstractThis thesis studies the subject of maritime target detection using synthetic aperture radar (SAR). In real maritime surveillance and monitoring systems, an advanced operational target detector is a key component and must be able to work in unknown target situations and under various non-homogeneous sea surface conditions. In this study, truncated statistics and a modified segmentation stage are adopted in the constant false alarm rate (CFAR) detection scheme, which is proved to be an optimal solution to simultaneously address the frequently encountered detection issues, i.e., the capture effect in multiple-target situations, and the clutter edge effect due to meteorological and oceanographic phenomena. Compared to the conventional CFAR detectors, the proposed target detection algorithm is able to operate in various contaminated non-homogeneous environments, provide rigorous statistical analysis of local background clutter, and deliver improved robust detection performance.en_US
dc.description.sponsorshipFunding from the Norwegian Research Council through the ArcticEO project.en_US
dc.descriptionPaper I of this thesis is not available in Munin.<br> I. Tao D, Anfinsen SN, Brekke C. A comparative study of sea clutter covariance matrix estimators. Available in <a href=http://dx.doi.org/10.1109/LGRS.2013.2284822>IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 5, pp. 1010–1014, May 2014</a>en_US
dc.identifier.isbn978-82-8236-198-9 (trykt) og 978-82-8236-199-6 (pdf)
dc.identifier.urihttps://hdl.handle.net/10037/9245
dc.identifier.urnURN:NBN:no-uit_munin_8800
dc.language.isoengen_US
dc.publisherUiT Norges arktiske universiteten_US
dc.publisherUiT The Arctic University of Norwayen_US
dc.rights.accessRightsopenAccess
dc.subject.courseIDDOKTOR-004en_US
dc.subjectVDP::Mathematics and natural science: 400::Mathematics: 410::Analysis: 411en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Matematikk: 410::Analyse: 411en_US
dc.subjectVDP::Mathematics and natural science: 400::Mathematics: 410::Statistics: 412en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Matematikk: 410::Statistikk: 412en_US
dc.subjectVDP::Mathematics and natural science: 400::Information and communication science: 420::Mathematical modeling and numerical methods: 427en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Matematisk modellering og numeriske metoder: 427en_US
dc.subjectVDP::Mathematics and natural science: 400::Information and communication science: 420::Simulation, visualization, signal processing, image processing: 429en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Simulering, visualisering, signalbehandling, bildeanalyse: 429en_US
dc.subjectSynthetic aperture radaren_US
dc.subjectTarget detectionen_US
dc.subjectSea clutteren_US
dc.subjectConstant false alarm rateen_US
dc.subjectStatistical modelingen_US
dc.subjectTruncated statisticsen_US
dc.subjectNon-homogeneousen_US
dc.subjectContaminationen_US
dc.titleMaritime Target Detection in Non-homogeneous Sea Clutter Environments based on Single- and Multi-polarization Synthetic Aperture Radar Dataen_US
dc.typeDoctoral thesisen_US
dc.typeDoktorgradsavhandlingen_US


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