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An evaluation of several different classification schemes - Their parameters and performanceThe overall objective of this study was to apply and evaluate several of the currently available classification schemes for crop identification. The approaches examined were: (1) a per point Gaussian maximum likelihood classifier, (2) a per point sum of normal densities classifier, (3) a per point linear classifier, (4) a per point Gaussian maximum likelihood decision tree classifier, and (5) a texture sensitive per field Gaussian maximum likelihood classifier. Three agricultural data sets were used in the study: areas from Fayette County, Illinois, and Pottawattamie and Shelby Counties in Iowa. The segments were located in two distinct regions of the Corn Belt to sample variability in soils, climate, and agricultural practices.
Document ID
19800038283
Acquisition Source
Legacy CDMS
Document Type
Conference Proceedings
Authors
Scholz, D.
(Purdue Univ. West Lafayette, IN, United States)
Fuhs, N.
(Purdue Univ. West Lafayette, IN, United States)
Hixson, M.
(Purdue University West Lafayette, Ind., United States)
Date Acquired
August 10, 2013
Publication Date
January 1, 1979
Subject Category
Earth Resources And Remote Sensing
Meeting Information
Meeting: International Symposium on Remote Sensing of Environment
Location: Ann Arbor, MI
Start Date: April 23, 1979
End Date: April 27, 1979
Accession Number
80A22453
Funding Number(s)
CONTRACT_GRANT: NAS9-15466
Distribution Limits
Public
Copyright
Other

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