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dc.contributor.authorPhillips, Rhonda D.en_US
dc.contributor.authorWatson, Layne T.en_US
dc.contributor.authorWynne, Randolph H.en_US
dc.contributor.authorRamakrishnan, Narenen_US
dc.date.accessioned2013-06-19T14:36:53Z
dc.date.available2013-06-19T14:36:53Z
dc.date.issued2009
dc.identifierhttp://eprints.cs.vt.edu/archive/00001073/en_US
dc.identifier.urihttp://hdl.handle.net/10919/19573
dc.description.abstractThis paper describes in detail the continuous iterative guided spectral class rejection (CIGSCR) classification method based on the iterative guided spectral class rejection (IGSCR) classification method for remotely sensed data. Both CIGSCR and IGSCR use semisupervised clustering to locate clusters that are associated with classes in a classification scheme. In CIGSCR and IGSCR, training data are used to evaluate the strength of the association between a particular cluster and a class, and a statistical hypothesis test is used to determine which clusters should be associated with a class and used for classification and which clusters should be rejected and possibly refined. Experimental results indicate that the soft classification output by CIGSCR is reasonably accurate (when compared to IGSCR), and the fundamental algorithmic changes in CIGSCR (from IGSCR) result in CIGSCR being less sensitive to input parameters that influence iterations. Furthermore, evidence is presented that the semisupervised clustering in CIGSCR produces more accurate classifications than classification based on clustering without supervision.en_US
dc.format.mimetypeapplication/pdfen_US
dc.publisherDepartment of Computer Science, Virginia Polytechnic Institute & State Universityen_US
dc.subjectAlgorithmsen_US
dc.subjectData structuresen_US
dc.titleContinuous Iterative Guided Spectral Class Rejection Classification Algorithm: Part 2en_US
dc.typeTechnical reporten_US
dc.identifier.trnumberTR-09-10en_US
dc.type.dcmitypeTexten_US
dc.identifier.sourceurlhttp://eprints.cs.vt.edu/archive/00001073/01/cigscr2.pdf


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