A Hybrid Bayesian Network Classifier for Multi-source Remote Sensing Data in Land Use Classification
LI Feng 1， GAO Zhao-liang 2,3
1.Fujian Agriculture and Forestry University， Fuzhou 350002, China； 2.The State Key Laboratory for Information Engineering in Surveying, Mapping and Romote Sensing， Wuhan 430079, China； 3.Fuzhou Investigation and Surveying Institute， Fuzhou 350003, China
Abstract： 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.
李凤, 高昭良. 面向土地利用分类的多源遥感数据混合贝叶斯网络分类器[J]. 国土资源遥感, 2011, 23(2): 47-52.
LI Feng, GAO Zhao-Liang. A Hybrid Bayesian Network Classifier for Multi-source Remote Sensing Data in Land Use Classification. REMOTE SENSING FOR LAND & RESOURCES, 2011, 23(2): 47-52.
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2011, Vol. 23 
