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ALLOCATION OF REMOTELY SENSED DATA USING MARKOV MODELS FOR IMAGE DATA AND PIXEL LABELS
Author(s) -
Kiiveri H.T.,
Campbell N.A.
Publication year - 1992
Publication title -
australian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 0004-9581
DOI - 10.1111/j.1467-842x.1992.tb01053.x
Subject(s) - pixel , computer science , exploit , image (mathematics) , a priori and a posteriori , independence (probability theory) , property (philosophy) , conditional independence , markov chain , artificial intelligence , bayesian probability , spatial correlation , posterior probability , pattern recognition (psychology) , data mining , mathematics , statistics , machine learning , telecommunications , philosophy , computer security , epistemology
Summary For remotely sensed data, this paper reviews the Bayesian approach to the allocation of picture elements (pixels) to groups. Group labels are assumed a priori to be spatially correlated and, conditional on the labels, the image data are also assumed to be spatially correlated. The models considered have the property that the posterior distribution of the pixel labels given the image data inherits conditional independence constraints. Two allocation algorithms which exploit this fad are discussed. These algorithms are based on maximising the posterior distribution, and involve the use of neighbouring image and label data to update the label of any given pixel. The effect of spatial correlation in the image data on allocation performance is examined.