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.
[1]Chow C K,Liu C N.Approximating Discrete Probability Distributions with Dependence Trees[J].IEEE Transactions on Information Theory,1968,14(3):462-467.[2]梁静,张桂峰.基于信息熵的遥感影像特征离散化方法[J].地理空间信息,2006,4(3):9-11.[3]Pedro S D D,Hruschka E R,Flruschka E R.WNB:A Weighted Naive Bayesian Classifier[C]//Proceedings of 7th International Conference on Intelligent Systems Design and Applications(ISDA 2007),Rio de Janeir,Brazil:University Estado Rio de Janeiro,2007:138-142.[4]Pedro Domingos,Michael Pazzani.On the Optimality of the Simple Bayesian Classifier Under Zero-one Loss[J].Machine Learning,1997,29(2-3):103-130.[5]张连文,郭海鹏.贝叶斯网引论[M].北京:科学出版社,2006.[6]Cheng Jie,Greiner Russell,Kelly Jonathan,et al.Learning Bayesian Networks from Data,an Information-theory Based Approach[J].Artificial Intelligence,2002,137(1-2):43-90.[7]虞欣.贝叶斯网络在航空影像纹理分类中应用研究[D].武汉:武汉大学,2008.[8]普雷斯,S·詹姆士.贝叶斯统计学:原理、模型及应用[M].北京:中国统计出版社,1992.[9]Buntine W.A Guide to the Literature on Learning Probabilistic Networks from Data[J].IEEE Transactions on Knowledge and Data Engineering,1996,8(2):195-210.[10]洪楠,侯军.MINITAB统计分析教程[M].北京:电子工业出版社,2007.[11]王正兴,刘闯,Huete AIfredo.植被指数研究进展:从AVHRR-NDVI到MODIS-EVI[J].生态学报,2003,23(5):979-987.