A Hybrid Bayesian Network Classifier for Multi-source Remote Sensing Data in Land Use Classification
Gao Zhao-liang · Guotu ziyuan yaogan · 2011
It is necessary that all variables be considered as discrete variables,or discretization be conducted in a traditional discrete Bayesian network classifier.The information loss in discretization is inevitable,and the discretization of continuous variables will lead to dramatic expansion of search space and great expenses in computation and storage in multi-source data processing and analysis.To solve these problems,the authors have developed the Hybrid Bayesian network classifier for land use classification,which first conducts normal distribution test for all variables in the study area.For the variables that meet Gaussian distribution assumptions,the authors do not discrete them and regard them as continuous variables.Parameter learning of discrete variables and that of continuous variables are carried out respectively,and then the parameters are merged.These parameters are used for reasoning and classification of Bayesian network at last.Experiments of land use classification in Fujian show that the model is superior to the traditional discrete Bayesian network classifier,and hence has great research and application value.