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Gaussian maximum likelihood and contextual classification algorithms for multicrop classificationThe paper reviews some of the ways in which context has been handled in the remote-sensing literature, and additional possibilities are introduced. The problem of computing exhaustive and normalized class-membership probabilities from the likelihoods provided by the Gaussian maximum likelihood classifier (to be used as initial probability estimates to start relaxation) is discussed. An efficient implementation of probabilistic relaxation is proposed, suiting the needs of actual remote-sensing applications. A modified fuzzy-relaxation algorithm using generalized operations between fuzzy sets is presented. Combined use of the two relaxation algorithms is proposed to exploit context in multispectral classification of remotely sensed data. Results on both one artificially created image and one MSS data set are reported.
Document ID
19880029890
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
Authors
Di Zenzo, Silvano
(IBM Italia S.p.A. Rome, Italy)
Bernstein, Ralph
(IBM Scientific Center Palo Alto, CA, United States)
Kolsky, Harwood G.
(California, University Santa Cruz, United States)
Degloria, Stephen D.
(Cornell University Ithaca, NY, United States)
Date Acquired
August 13, 2013
Publication Date
November 1, 1987
Publication Information
Publication: IEEE Transactions on Geoscience and Remote Sensing
Volume: GE-25
ISSN: 0196-2892
Subject Category
Earth Resources And Remote Sensing
Accession Number
88A17117
Funding Number(s)
CONTRACT_GRANT: NAS5-27377
CONTRACT_GRANT: NAS5-27355
Distribution Limits
Public
Copyright
Other

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