Detection of buried objects in multi-temporal and multi-band infrared imagery using dynamic bayesian networks

A direct change detection method that utilizes the dynamic Bayesian network (DBNs) is proposed to detect buried objects. The DBNs uses the time series dynamic data to produce credible probabilistic reasoning, and is developed to utilize the IR images obtained by different band and temporal. The proposed method offers a way to change detection analysis from the static viewpoint to the dynamic viewpoint, which can input and deal with more than two multitemporal images simultaneously which are featured by multi-band. The origin of thermal contrast in infrared imaging between the buried objects and background is illuminated on the theory of infrared radiation. The differences of temperature can be captured by multi-temporal and multi-band infrared images. The IR images of the regions of interest (ROI) acquired at three different times as inputs to detect buried objects using multi-temporal direct change detection based on physical principle of infrared imaging. The experimental results indicate that the change detection method based on DBNs is an effective to buried objects detection.