期刊文献+

基于贝叶斯网络分类的土壤盐渍化遥感监测

Remote Sensing Monitoring of Soil Salinization Based on Bayesian Network Classification
下载PDF
导出
摘要 贝叶斯网络是一种将贝叶斯概率方法和有向无环图的网络拓扑结构有机结合的概率模型。采用贝叶斯网络分类对具有典型干旱特征的库车县土壤盐渍化情况进行监测,首先应用条件独立性测试原理建立贝叶斯网络结构,把研究区遥感数据进行离散化,然后应用贝叶斯定理作为分类原则,将每个像元分为像元最大概率的类别。研究结果表明该方法分类6种地类的整体分类精度达到96%,为该区盐渍地面积、空间分布等特征监测提供了较好的依据。 The Bayesian network is one kind of probability model, which is based on probability theory and graph theory. The network shows that random variables are nodes and conditional dependencies are edges in a directed acyclic graph. In this paper, soil salinization of Kucha county in arid region was monitored with the Bayesian network classifier. Firstly, using the conditional independence test built the Bayesian network structure, and desensitizes remote sensing data of study area. Secondly, applying Bayesian network theorem as classification principle classifies every single pixel to a pixel of maximum probability sort. The results indicate that the classification accuracy is above 95%, which provides reasonable base for monitoring the areas and spatial distribution of salinization soil.
出处 《云南环境科学》 2006年第4期52-55,共4页 Yunnan Environmental Science
基金 新疆维吾尔自治区高校科研计划科学研究重点项目(XJEDU2004I06)
关键词 贝叶斯网络 土壤盐渍化 遥感监测 Bayesian network soil salinization remote sensing monitoring
  • 相关文献

参考文献9

二级参考文献42

  • 11.Chickering D. Learning equivalence classes of Bayesian networks structures. In: Horvitz E, Jensen F ed. Proceedings of the 12th Conference on Uncertainty in Artificial Intelligence. San Francisco, CA: Morgan Kaufmann Publishers, Inc., 1996. 54~61
  • 22.Geriger D, Hekerman D. A charactererization of the Dirichlet distribution with application to learning Bayesian networks. In: Besnard P, Hanks S eds. Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann Publishers Inc., 1995. 196~207
  • 33.Heckman D. A Bayesian approach for learning causal networks. In: Besnard P, Hanks S eds. Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence. San Francisco, CA: Morgan Kaufmann Publishers, Inc., 1995. 285~295
  • 44.Heckman D, Geiger D, Chickering D. Learning Bayesian networks: the combination of knowledge and statistical data. Machine Learning, 1995,20(3):197~243
  • 55.Heckman D, Shachter R. Decision-Theoretic foundations for causal reasoning. Journal of Artificial Intelligence Research, 1995,3:405~430
  • 66.Heckman D, Mandani A, Wellman M. Real-World applications of Bayesian networks. Communications of the ACM, 1995,38(3):38~45
  • 77.Buntine W. Theory refinement on Bayesian networks. In: Proceedings of the 7th Conference on Uncertainty in Artificial Intelligence. Los Angeles, CA: Morgan Kaufmann Publishers, Inc., 1991. 52~61
  • 88.Cooper G, Herskovits E. A Bayesian method for the introduction of probabilistic networks from data. Machine Learning, 1992,9(4):309~347
  • 99.Russell S, Binder J, Koller D et al. Local learning in probabilistic networks with hidden variables. In: Cooper G F, Moral S ed. Proceedings of the 14th International Joint Conference on Artificial Intelligence. San Francisco, CA: Morgan Kaufmann Publishers, Inc., 1998. 1146~1152
  • 101999-03-15

共引文献230

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部