Graph-Based Registration, Change Detection, and Classification in Very High Resolution Multitemporal Remote Sensing Data
In this paper, we propose a modular, scalable, metric-free, single-shot change detection/registration method. The developed framework exploits the relation between the registration and change detection problems, while under a fruitful synergy, the coupling energy term constrains adequately both tasks. In particular, through a decomposed interconnected graphical model, the registration similarity constraints are relaxed in the presence of change detection. Moreover, the deformation space is discretized, while efficient linear programming and duality principles are used to optimize a joint solution space where local consistency is imposed on the deformation and the detection space as well. The proposed formulation is able to operate in a fully unsupervised manner addressing binary change detection problems, i.e., change or no-change with respect to different similarity metrics. Furthermore, the framework has been formulated to address automatically the detection of from-to change trajectories under a supervised setting. Promising results on large scale experiments demonstrate the extreme potentials of our method.