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Spatial estimation from remotely sensed data via empirical Bayes modelsMultichannel satellite image data, available as LANDSAT imagery, are recorded as a multivariate time series (four channels, multiple passovers) in two spatial dimensions. The application of parametric empirical Bayes theory to classification of, and estimating the probability of, each crop type at each of a large number of pixels is considered. This theory involves both the probability distribution of imagery data, conditional on crop types, and the prior spatial distribution of crop types. For the latter Markov models indexed by estimable parameters are used. A broad outline of the general theory reveals several questions for further research. Some detailed results are given for the special case of two crop types when only a line transect is analyzed. Finally, the estimation of an underlying continuous process on the lattice is discussed which would be applicable to such quantities as crop yield.
Document ID
19850007947
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Hill, J. R.
(Texas Univ. Austin, TX, United States)
Hinkley, D. V.
(Texas Univ. Austin, TX, United States)
Kostal, H.
(Texas Univ. Austin, TX, United States)
Morris, C. N.
(Texas Univ. Austin, TX, United States)
Date Acquired
August 12, 2013
Publication Date
January 1, 1984
Publication Information
Publication: Texas A and M Univ. Proc. of the 2nd Ann. Symp. on Math. Pattern Recognition and Image Analysis Program
Subject Category
Statistics And Probability
Accession Number
85N16256
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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