The traditional post classification comparison change detection of remote sensing is greatly restricted by the classification accuracy which is influenced by the uncertainty of many factors such as the atmospheric condition

the correlation between the bands of remote sensing data etc.The prior knowledge is often introduced into the classification in order to improve the accuracy.The Bayesian Networks model is a new model for data expression and learning.It has no strict precondition of normal distribution of the input data and can increase the classification accuracy efficiently though adjusting the prior probability density dynamically.The Bayesian Networks classification algorithm was developed in this paper

taking the Landsat TM data in Beijing acquired on May 29

th

1996 and May 19

th

2001 as an example in detail and then the change detection using the temporal remote sensing data was realized.The experimental result indicates that the post classification comparison based on Bayesian Network classification algorithm is a newly effective approach for remote sensing imageries change detection.