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A structural-stochastic model for the analysis and synthesis of cloud imagesA structural-stochastic image model is developed for the analysis and synthesis of cloud images. The ability of the model to characterize the visual appearance of cloud fields observed by satellite with a limited number of parameters is demonstrated. The model merges structural and stochastic information, the stochastic model acting as a local statistical operator applied to the output of the structural model. The structural or large-scale organization of the scene is retrieved from the two-dimensional Fourier representation of the digital image. The pattern generated by the major Fourier components provides a first guess of the scene. The stochastic aspect is described by a Markov model of texture that assumes a binomial probability distribution for the local grey-level variability. This Markov model provides four parameters that represent the clustering strength in the horizontal, vertical and diagonal directions. These parameters are estimated by a standard maximum-likelihood technique. The image can be reproduced with a fair degree of verisimilitude from these parameters. The data compression factor is of the order of one hundred to several hundreds.
Document ID
19870025400
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
Authors
Garand, L.
(Wisconsin Univ. Madison, WI, United States)
Weinman, J. A.
(Wisconsin, University Madison, United States)
Date Acquired
August 13, 2013
Publication Date
July 1, 1986
Publication Information
Publication: Journal of Climate and Applied Meteorology
Volume: 25
ISSN: 0733-3021
Subject Category
Meteorology And Climatology
Accession Number
87A12674
Funding Number(s)
CONTRACT_GRANT: NAGW-380
CONTRACT_GRANT: NSF ATM-82-12424
Distribution Limits
Public
Copyright
Other

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