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    Continuous Iterative Guided Spectral Class Rejection Classification Algorithm: Part 2

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    Downloads: 49
    TR number
    TR-09-10
    Date
    2009
    Author
    Phillips, Rhonda D.
    Watson, Layne T.
    Wynne, Randolph H.
    Ramakrishnan, Naren
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    Abstract
    This 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.
    URI
    http://hdl.handle.net/10919/19573
    Collections
    • Computer Science Technical Reports [1036]
    • Technical Reports, Center for Environmental Applications of Remote Sensing (CEARS) [3]

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