Abstract: Since there is deficiency in parameter estimation and classification of traditional change detection approach of remote
sensing image based on Bayes decision rules，this paper adopts the method of dynamic updating the parameters and
gains the posteriori probability function through these parameters.Then，computes the criterion function through transforming
the posteriori probability function to fuzzy subordinate function.Finally，Estimates the pixels that haven’t been classified to extract
the changing region from remote sensing images.Through experiments，it proves that the change detection approach of
remote sensing images based on fuzzy Bayes decision rules has a higher precision comparing to traditional change detection
approach of remote sensing image based on Bayes decision rules and ERDAS．

摘要： 针对传统基于Bayes 决策规则的遥感影像变化检测方法中参数估计的不足以及分类过程中的硬划分问题，采用动态更新变化和未变化两类像元模糊子集的方法，实现对两类像元模糊子集中参数的动态更新，利用估计参数获得各子集的后验概率函数，再将后验概率函数转化为模糊子集的模糊隶属函数，从而获得各子集的指标函数，根据指标函数对影像中未分类的像元值进行判断，实现遥感影像的变化区域提取。实验结果表明：与现有的基于Bayes 决策规则的遥感影像变化检测方法及ERDAS 软件生成结果相比，提出的方法具有更好的变化检测精度。