Remote Sensing Change Detection Using Bayesian Networks
利用贝叶斯网络进行遥感变化检测

In recent years, the Bayesian network has been used in many study fields as a data-mining tool, but so far it is seldom used to process remote sensing data. In this paper, we introduce the algorithm about constructing Bayesian network classifier for remote sensing data based on the conditional mutual information test of different bands. The technical procedures of change detection with remote sensing data using Bayesian network are also presented, and the multi temporal Landsat TM data of Beijing acquired in 1994 and 2003 are taken as an example and performed with change detection. The change detection results show that from the year 1994 to 2003, 26.52% farmland of study area had been changed to urban land, 4.68% greenland was increased. The Directed Acyclic Graph (DAG) of Bayesian network describes the mutual information of multi-characteristic data, which synthesized the prior probability and sample information. The study results suggest that Bayesian network will be a newly effective approach for remote sensing data change detection.