Change detection from remote sensing images based on evidential reasoning

Theories of evidence have already been applied more or less successfully in the fusion of remote sensing images. In the classical evidential reasoning, all the sources of evidence and their fusion results are related with the same invariable (static) frame of discernment. Nevertheless, there are possible change occurrences through multi-temporal remote sensing images, and these changes need to be detected efficiently in some applications. The invariable frame of classical evidential reasoning can't efficiently represent nor detect the changes occurrences from heterogenous remote sensing images. To overcome this limitation, Dynamical Evidential Reasoning (DER) is proposed for the sequential fusion of multi-temporal images. A new state transition frame is defined in DER and the change occurrences can be precisely represented by introducing a state transition operator. The belief functions used in DER are defined similarly to those defined in the Dempster-Shafer Theory (DST). Two kinds of dynamical combination rules working in free model and constrained model are proposed in this new framework for dealing with the different cases. In the final, an experiment using three pieces of real satellite images acquired before and after an earthquake are provided to show the interest of the new approach.