Study on Dynamic Bayesian Networks for Multi-temporal Remote Sensing Change Detection
多时相遥感变化检测的动态贝叶斯网络研究

The Dynamic Bayesian Network(DBN),which uses the time-series dynamic data to produce credible probabilistic reasoning,is a method developed in 1990s based on the Bayesian network,and offers a way to change analysis from the static viewpoint to the dynamic viewpoint when we carry out remote sensing change detection.Grasping the development tendency,we explore how to use Dynamic Bayesian Networks for direct change detection of remote sensing data with multi-temporal features.Taking the Landsat TM remote sensing data of eastern Beijing area acquired in May of 1994,2001 and 2003 as an example,we introduce in detail the method to do multi-temporal remote sensing direct change detection using Dynamic Bayesian Networks.The good result indicates that: the DBN-based direct change detection algorithm can input and handle remote sensing data of more than two time phases simultaneously,and it describes the relationship among the features and states of different time phases by means of probability and directed acyclic graphs.