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Estimation of context for statistical classification of multispectral image dataRecent investigations have demonstrated the effectiveness of a contextual classifier that combines spatial and spectral information employing a general statistical approach. This statistical classification algorithm exploits the tendency of certain ground cover classes to occur more frequently in some spatial contexts than in others. Indeed, a key input to this algorithm is a statistical characterization of the context: the context function. An unbiased estimator of the context function is discussed which, besides having the advantage of statistical unbiasedness, has the additional advantage over other estimation techniques of being amenable to an adaptive implementation in which the context-function estimate varies according to local contextual information. Results from applying the unbiased estimator to the contextual classification of three real Landsat data sets are presented and contrasted with results from noncontextual classifications and from contextual classifications utilizing other context-function estimation techniques.
Document ID
19830033635
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
Authors
Tilton, J. C.
(Computer Sciences Corp. Silver Spring, MD, United States)
Vardeman, S. B.
(Iowa State University of Science and Technology, Ames, IA, United States)
Swain, P. H.
(Purdue University West Lafayette, IN, United States)
Date Acquired
August 11, 2013
Publication Date
October 1, 1982
Publication Information
Publication: IEEE Transactions on Geoscience and Remote Sensing
Volume: GE-20
Subject Category
Earth Resources And Remote Sensing
Accession Number
83A14853
Funding Number(s)
CONTRACT_GRANT: NAS9-15466
CONTRACT_GRANT: NSF MCS-78-04366
Distribution Limits
Public
Copyright
Other

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