Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-1397
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dc.contributor.authorSun, Wanxiao
dc.date.accessioned1999-12-31T23:00:00Z
dc.date.available2000-01-01T00:00:00Z
dc.date.issued2000
dc.identifier.urihttps://openscience.ub.uni-mainz.de/handle/20.500.12030/1399-
dc.description.abstractSatellite image classification involves designing and developing efficient image classifiers. With satellite image data and image analysis methods multiplying rapidly, selecting the right mix of data sources and data analysis approaches has become critical to the generation of quality land-use maps. In this study, a new postprocessing information fusion algorithm for the extraction and representation of land-use information based on high-resolution satellite imagery is presented. This approach can produce land-use maps with sharp interregional boundaries and homogeneous regions. The proposed approach is conducted in five steps. First, a GIS layer - ATKIS data - was used to generate two coarse homogeneous regions, i.e. urban and rural areas. Second, a thematic (class) map was generated by use of a hybrid spectral classifier combining Gaussian Maximum Likelihood algorithm (GML) and ISODATA classifier. Third, a probabilistic relaxation algorithm was performed on the thematic map, resulting in a smoothed thematic map. Fourth, edge detection and edge thinning techniques were used to generate a contour map with pixel-width interclass boundaries. Fifth, the contour map was superimposed on the thematic map by use of a region-growing algorithm with the contour map and the smoothed thematic map as two constraints. For the operation of the proposed method, a software package is developed using programming language C. This software package comprises the GML algorithm, a probabilistic relaxation algorithm, TBL edge detector, an edge thresholding algorithm, a fast parallel thinning algorithm, and a region-growing information fusion algorithm. The county of Landau of the State Rheinland-Pfalz, Germany was selected as a test site. The high-resolution IRS-1C imagery was used as the principal input data.en_GB
dc.language.isoeng
dc.rightsInCopyrightde_DE
dc.rights.urihttps://rightsstatements.org/vocab/InC/1.0/
dc.subject.ddc910 Geografiede_DE
dc.subject.ddc910 Geography and travelen_GB
dc.titleA new information fusion method for land-use classification using high resolution satellite imageryen_GB
dc.typeDissertationde_DE
dc.identifier.urnurn:nbn:de:hebis:77-42
dc.identifier.doihttp://doi.org/10.25358/openscience-1397-
jgu.type.dinitypedoctoralThesis
jgu.type.versionOriginal worken_GB
jgu.type.resourceText
jgu.organisation.departmentFB 09 Chemie, Pharmazie u. Geowissensch.-
jgu.organisation.year2000
jgu.organisation.number7950-
jgu.organisation.nameJohannes Gutenberg-Universität Mainz-
jgu.rights.accessrightsopenAccess-
jgu.organisation.placeMainz-
jgu.subject.ddccode910
opus.date.accessioned1999-12-31T23:00:00Z
opus.date.modified1999-12-31T23:00:00Z
opus.date.available2000-01-01T00:00:00
opus.organisation.stringFB 09: Chemie, Pharmazie und Geowissenschaften: FB 09: Chemie, Pharmazie und Geowissenschaftende_DE
opus.identifier.opusid4
opus.institute.number0900
opus.metadataonlyfalse
opus.type.contenttypeDissertationde_DE
opus.type.contenttypeDissertationen_GB
jgu.organisation.rorhttps://ror.org/023b0x485
Appears in collections:JGU-Publikationen

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