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dc.contributor.advisorLegarsky, Justin J.eng
dc.contributor.authorHuang, Heng, 1977-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.] To further increase the classification accuracies, radar image processing techniques were investigated to preprocess the Radarsat data before classification. Eight processing techniques were applied to Radarsat data at various windows from 3 x 3 to 25 x 25 pixels. For a single radar feature, the Entropy processing at window size 13 x 13 provides the best overall land cover classification accuracy improvement when fused with the Landsat imagery. For multiple radar features, a higher accuracy improvement was found when combining the features (i.e., 13 x 13 Entropy, 9 x 9 data range, 19 x 19 mean) with the Landsat data. This study introduces an approach of fusing Landsat data with multiple Radarsat features to the land cover classification practice. Post-classification techniques were studied for land cover classification maps. Several weighted kernels were developed for the majority filtering process. The method evaluates the correlation between neighbor pixels according to the distance and further improves the classification accuracy. For the St. Louis study area, the Gaussian weighted kernel increases the overall land classification accuracy compared to the Landsat images. Post-classification smoothing of the sensor fusion result (Landsat and radar feature combination) further increases the accuracy. A decadal change analysis was also conducted for the St. Louis, Missouri area using Landsat imagery and census population data. This study proposes a methodology to integrate remotely sensed and census data in urban change analysis. The assessment can provide information that can highlight priority urban growth regions. The analysis shows strong correlation between population and land cover changes, which indicates the potential of satellite imagery to generate the physical feature input for tele-traffic forecasting of a cellular network.eng
dc.identifier.merlinb57262421eng
dc.identifier.urihttps://hdl.handle.net/10355/5812
dc.identifier.urihttps://doi.org/10.32469/10355/5812eng
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 sensingeng
dc.subject.lcshImage processingeng
dc.subject.lcshGeospatial dataeng
dc.titleLand cover classification from satellite imagery, and its applications in cellular network planningeng
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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