Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Real-world applications of Bayesian networks
Communications of the ACM
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
Learning the Dimensionality of Hidden Variables
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Review: Bayesian networks in environmental modelling
Environmental Modelling & Software
Integrating spatial relations into case-based reasoning to solve geographic problems
Knowledge-Based Systems
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In recent years, the Bayesian network approach has been used as a data-mining tool in many information fields, but it has rarely been used to process remote sensing data. In this paper, we introduce a Bayesian network classifier for remote sensing data change detection. Using the conditional independence (CI) test we can find out relationships among the attributes and construct a Bayesian network that incorporates these relationship constraints. After geometric correction and radiometric normalization, a Bayesian network change detection system based on CI test algorithm is developed and applied to two temporal Landsat TM data acquired in 1994 and 2003 of Beijing area, and the overall change detection classification accuracy can get 92%. The experimental results show that Bayesian network is a newly effective approach for remote sensing data change detection.