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dc.contributor.authorAshish, Dev
dc.date.accessioned2014-03-05T16:03:31Z
dc.date.available2014-03-05T16:03:31Z
dc.date.issued2002-08
dc.identifier.otherashish_dev_200208_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/ashish_dev_200208_ms
dc.identifier.urihttp://hdl.handle.net/10724/29481
dc.description.abstractThis thesis describes the study of Artificial Neural Network (ANN) based techniques for the classification of aerial images for various types of land-use. In this study both gray-scale and multispectral aerial images were used in land-use classification. Three approaches were used for the preparation of the data as inputs to the ANN, including histograms of the pixel intensities, textural parameters extracted from the image, and matrices of pixels for spatial information. The approach using textural parameters was found to be the best for both gray-scale and multispectral image classification. A probabilistic neural network was employed. A high level of accuracy was achieved with both gray-scale (92%) and multispectral images (89%).
dc.languageLand-use classification of aerial images using artificial neural networks
dc.publisheruga
dc.rightspublic
dc.subjectAerial remote sensing
dc.subjectartificial neural networks
dc.subjectimage classification
dc.subjectimage processing.
dc.titleLand-use classification of aerial images using artificial neural networks
dc.typeThesis
dc.description.degreeMS
dc.description.departmentArtificial Intelligence Center
dc.description.majorArtificial Intelligence
dc.description.advisorRonald W. McClendon
dc.description.committeeRonald W. McClendon
dc.description.committeeGerrit Hoogenboom
dc.description.committeeWalter D. Potter


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