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dc.contributor.advisorDavis, Curt H.eng
dc.contributor.authorJin, Xiaoying, 1975-eng
dc.date.issued2005eng
dc.date.submitted2005 Falleng
dc.descriptionThe entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.eng
dc.descriptionTitle from title screen of research.pdf file viewed on (November 15, 2006)eng
dc.descriptionIncludes bibliographical references.eng
dc.descriptionVita.eng
dc.descriptionThesis (Ph.D.) University of Missouri-Columbia 2005.eng
dc.descriptionDissertations, Academic -- University of Missouri--Columbia -- Electrical engineering.eng
dc.description.abstract[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Recently available high-resolution commercial satellite imagery provides an important new data source for remote sensing applications. Automated feature extraction (AFE) techniques can assist human analysts by rapidly locating geospatial information and have the potential to significantly reduce the amount of time to process and analyze geospatial data. In this research, we have designed and developed systems for automatic extraction of man-made objects (roads, buildings and vehicles) from high-resolution satellite imagery. We conclude that AFE can be greatly enriched and improved by multiinformation fusion and/or multi-cue integration. For road extraction and building extraction respectively, multiple detectors were developed and the extraction performance was greatly improved using multi-detector fusion from different information sources. For vehicle detection, a GIS road vector layer was used to incorporate contextual information and an implicit vehicle model including spectral and spatial characteristics was learned by a morphological shared-weight neural network. An important characteristic of our research on road and building extraction is that our extraction strategies are fully automated with only a few preset parameters. Compared with related research in these areas, the performance evaluations of our extraction systems are among the highest statistical values reported in literature thus far.eng
dc.identifier.merlinb57262470eng
dc.identifier.urihttps://hdl.handle.net/10355/5816
dc.identifier.urihttps://doi.org/10.32469/10355/5816eng
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartofcommunityUniversity of Missouri--Columbia. Graduate School. Theses and Dissertationseng
dc.rightsAccess to files is limited to the campuses of the University of Missouri.eng
dc.subject.lcshRemote-sensing imageseng
dc.subject.lcshRemote sensingeng
dc.titleAutomatic extraction of man-made objects from high-resolution satellite imagery by information fusioneng
dc.typeThesiseng
thesis.degree.disciplineElectrical and computer engineering (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelDoctoraleng
thesis.degree.namePh. D.eng


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