Multi-temporal Remote Sensing Change Detection Using Dynamic Bayesian Networks
Ouyang Yun①②; Ma Jian-wen①; Dai Qin①②
①Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing 100101, China; ②Graduate School, Chinese Academy of Sciences, Beijing 100049, China
Abstract：Utilizing Dynamic Bayesian Networks (DBNs) to deal with multi-temporal remote sensing data, the multi-temporal data of different time can be input simultaneously, and the classification and the acquirement of relationships between the output types can be finished simultaneously. Using the Landsat TM remote sensing data of Beijing eastern area acquired in May of 1994, 2001 and 2003 for the experiment, the experimental results indicate that the DBN-based change detection method is a new effective method of remote sensing change detection, and show its great potential for the research on the analysis of the dynamic changes of remote sensing time-series data.
2007, Vol. 29 
