Feature Reduction using a Singular Value Decomposition for the Iterative Guided Spectral Class Rejection Hybrid Classifier

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dc.contributor.authorPhillips, Rhonda D.en
dc.contributor.authorWatson, Layne T.en
dc.contributor.authorWynne, Randolph H.en
dc.contributor.authorBlinn, Christine E.en
dc.contributor.departmentCenter for Environmental Applications of Remote Sensing (CEARS)en
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2013-06-19T14:35:43Zen
dc.date.available2013-06-19T14:35:43Zen
dc.date.issued2007en
dc.description.abstractFeature reduction in a remote sensing dataset is often desirable to decrease the processing time required to perform a classification and improve overall classification accuracy. This work introduces a feature reduction method based on the singular value decomposition (SVD). This feature reduction technique was applied to training data from two multitemporal datasets of Landsat TM/ETM+ imagery acquired over a forested area in Virginia, USA and Rondonia, Brazil. Subsequent parallel iterative guided spectral class rejection (pIGSCR) forest/nonforest classifications were performed to determine the quality of the feature reduction. The classifications of the Virginia data were five times faster using SVDbased feature reduction without affecting the classification accuracy. Feature reduction using the SVD was also compared to feature reduction using principal components analysis (PCA). The highest average accuracies for the Virginia dataset (88.34%) and for the Rondonia dataset (93.31%) were achieved using the SVD. The results presented here indicate that SVDbased feature reduction can produce statistically significantly better classifications than PCA.en
dc.format.mimetypeapplication/pdfen
dc.identifierhttp://eprints.cs.vt.edu/archive/00000974/en
dc.identifier.sourceurlhttp://eprints.cs.vt.edu/archive/00000974/01/svdIEEETGRS07.pdfen
dc.identifier.trnumberTR-07-17en
dc.identifier.urihttp://hdl.handle.net/10919/19735en
dc.language.isoenen
dc.publisherDepartment of Computer Science, Virginia Polytechnic Institute & State Universityen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectAlgorithmsen
dc.subjectData structuresen
dc.titleFeature Reduction using a Singular Value Decomposition for the Iterative Guided Spectral Class Rejection Hybrid Classifieren
dc.typeTechnical reporten
dc.type.dcmitypeTexten

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