Markovian framework for structural change detection with application on detecting built-in changes in airborne images

In the paper we address the problem of change detection in airborne image pairs taken with significant time difference. In reconnaissance and exploration tasks, finding the slowly changing areas through a long tract of time is disturbed by the temporal parameter changes of the considered clusters. We introduce a new joint segmentation model, containing two layers corresponding to the same area of different far times and the detected change map. We tested this co-segmentation model considering two clusters on the photos: built-in and natural/cultivated areas. We propose a Bayesian segmentation framework which exploits not only the noisy class-descriptors in the independent images, but also creates links between the segmentation of the two pictures, ensuring to get smooth connected regions in the segmented images, and also in the change mask. The domain dependent part of the model is separated, therefore the proposed structure can be used for significantly different descriptors and problems also