An automatic registration framework using quantum particle swarm optimization for remote sensing images

Image registration is a fundamental problem for applications in remote sensing. In this paper, a new coarse-to-fine registration framework is proposed. In coarse registration step, Quantum Particle Swarm Optimization (QPSO) is used as optimizer to find best rigid parameters. The similarity measure is the Mutual Information (MI) of whole images. This method is valid under various displacements. In fine registration step, Harris detector is implemented to extract feature points in reference image, and template window is used to obtain corresponding points in sensed image. The parameters of the best affine transformation are estimated using the corresponding feature points. Analysis and experiments show our method leads to highly automatic registration, and is able to handle large displacements between remote sensing images fast and robustly.