A Novel Control Point Dispersion Method for Image Registration Robust to Local Distortion

Extracting well-distributed and precisely aligned control points (CPs) is extremely important for remote sensing image registration, particularly for high resolution images with large local distortion. Based on a theoretical analysis of estimation perturbation in transformation parameters, a novel CP dispersion approach is proposed to select high quality and uniformly distributed CPs. This approach retains a minimum spanning tree (MST) of the selected CPs during the algorithm and adds a new CP to the tree in each iteration until satisfying the convergence condition. Moreover, to acquire adequate number of CPs for the dispersion process, a coarse-to-fine matching approach is proposed. Experiment results indicate that the proposed method improves the match performance compared to other CP dispersion methods in terms of aligning accuracy.