C. Pal et al., The automated extraction of environmentally relevant features from digitalimagery using Bayesian multi-resolution analysis, ADV ENV RES, 5(4), 2001, pp. 435-444
In this paper, we discuss the use of hierarchical tree-structured Bayesian
networks for integrating knowledge concerning contextual relationships betw
een environmentally relevant features extracted from digital imagery at mul
tiple resolution scales. In our model, conditional probability distribution
s over continuous valued observations are parameterized using a mixture of
multivariate Gaussian distributions. Separate classifiers for pixels and gr
oups of pixels are used as sub-components of the overall model. The Bayesia
n formalism allows models to be composed in a systematic and statistically
sound manner. We illustrate how this approach can be used to resolve ambigu
ity leading to classification errors and thus improve techniques for the cl
assification of land use from aerial imagery. We present an example relevan
t to ecosystem analysis, the monitoring of urban growth and the automatic g
eneration of input parameters for hydrologic models. (C) 2001 Elsevier Scie
nce Ltd. All rights reserved.