遥感数据的贝叶斯网络分类研究

由于遥感成像过程的复杂性，遥感数据中包含了一定程度的不确定性因素。利用最大似然分类器处理遥感数据时分类精度受一定的影响, 为了提高分类精度往往需要引入先验知识。贝叶斯网络是一个带有概率注释的有向无环图，可以动态地对先验概率密度修正，提高分类精度，也没有严格的数据正态分布前提要求，适合处理不完整复杂的数据。该文介绍了利用贝叶斯网络对遥感数据进行分类处理的算法和技术过程。分类结果表明:贝叶斯网络具有稳定的数学基础，是一种可供遥感信息处理领域利用的有效新方法。

The Study on Remote Sensing Data Classification Using Bayesian Network

Because of the complexity in satellite remote sensing imaging system,some uncertainties or mixed spectrum information are contained in the data.By using maximal likelihood classification to process remote sensing data,the result accuracy of the classification is affected.In order to improve the accuracy of the classification,prior knowledge is needed to modify the probability.Bayesian network is composed of directed acyclic graph and probability chart;it can modify the prior probability density dynamically and improve the accuracy of classification.In this paper,a technical procedure is demonstrated that using Bayesian network to process the remote sensing data,the classification results prove that Bayesian network has solid mathematics base and can be a new effective methods for remote sensing data processing.