A Combined Global and Local Approach for Automated Registration of High-Resolution Satellite Images Using Optimum Extrema Points

Here we present an automated image-to-image registration method for high-resolution satellite images that combines a global affine transformation with non-linear local warping. We also present a novel feature matching method based on local image feature similarity, the spatial relationship amongst local extrema points (EPs), as well as k-sigma editing techniques to select optimum EPs for global affine transformation. The global affine coefficients are determined using the EP solutions that have similar affine transformations. Finally, local image warping is performed to adjust nonlinear distortions in the imagery caused by topography and/or sensor viewing geometry variations between scenes. Experiments and evaluation results from five test sites show that feature points can be automatically extracted and correctly matched between the input and reference images. The registration accuracy is within 2 pixels and is improved for areas with significant topographic variations.