Applications of fourier-based features for classification of synthetic aperture radar imagery

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dc.contributor.advisor Schott, John Ehrhard, David G. 2010-03-09T14:19:25Z 2010-03-09T14:19:25Z 1992-09
dc.description.abstract A method for segmenting synthetic aperture radar (SAR) images has been developed to operate primarily in the frequency domain. It is based on and was tested against a similar method which involves isolating information of the frequency-domain image that defines unique textural features within a class. The comparison involved classifying four simple vegetation SAR scenes with both segmentation methods. A statistical test was then performed against the null hypothesis that the new textural segmentation method is as accurate or more accurate than the original method based on random pixel classification results. All tests concluded that the texture extraction methods are not statistically different Both methods were implemented on a mainframe computer and are computationally intensive, but the new method may be implemented optically more easily. en_US
dc.language.iso en_US en_US
dc.relation RIT Scholars content from RIT Digital Media Library has moved from to RIT Scholar Works, please update your feeds & links!
dc.subject Digital imagery en_US
dc.subject Image classification en_US
dc.subject SAR Images en_US
dc.subject Synthetic aperture radar en_US
dc.subject.lcc TK6592.S95 E37 1992
dc.subject.lcsh Synthetic aperture radar en_US
dc.subject.lcsh Image processing--Digital techniques en_US
dc.title Applications of fourier-based features for classification of synthetic aperture radar imagery en_US
dc.type Thesis en_US College of Imaging Arts and Sciences en_US
dc.description.department Center for Imaging Science en_US

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