The automated extraction of environmentally relevant features from digital imagery using Bayesian multi-resolution analysis

Abstract In this paper, we discuss the use of hierarchical tree-structured Bayesian networks for integrating knowledge concerning contextual relationships between environmentally relevant features extracted from digital imagery at multiple resolution scales. In our model, conditional probability distributions over continuous valued observations are parameterized using a mixture of multivariate Gaussian distributions. Separate classifiers for pixels and groups of pixels are used as sub-components of the overall model. The Bayesian formalism allows models to be composed in a systematic and statistically sound manner. We illustrate how this approach can be used to resolve ambiguity leading to classification errors and thus improve techniques for the classification of land use from aerial imagery. We present an example relevant to ecosystem analysis, the monitoring of urban growth and the automatic generation of input parameters for hydrologic models.