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A method for classification of multisource data using interval-valued probabilities and its application to HIRIS dataA method of classifying multisource data in remote sensing is presented. The proposed method considers each data source as an information source providing a body of evidence, represents statistical evidence by interval-valued probabilities, and uses Dempster's rule to integrate information based on multiple data source. The method is applied to the problems of ground-cover classification of multispectral data combined with digital terrain data such as elevation, slope, and aspect. Then this method is applied to simulated 201-band High Resolution Imaging Spectrometer (HIRIS) data by dividing the dimensionally huge data source into smaller and more manageable pieces based on the global statistical correlation information. It produces higher classification accuracy than the Maximum Likelihood (ML) classification method when the Hughes phenomenon is apparent.
Document ID
19910006309
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Kim, H. (Purdue Univ. West Lafayette, IN, United States)
Swain, P. H. (Purdue Univ. West Lafayette, IN, United States)
Date Acquired
September 6, 2013
Publication Date
January 1, 1991
Publication Information
Publication: NASA, Goddard Space Flight Center, Multisource Data Integration in Remote Sensing
Subject Category
Earth Resources And Remote Sensing
Accession Number
91N15622
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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