Automatic and accurate registration of VHR optical and SAR images using a quadtree structure

In this article, we combined intensity- and feature-based similarity measures to co-register very-high-resolution (VHR) optical and SAR images. The global translation difference between optical and SAR images is minimized by applying a mutual information (MI) intensity-based similarity measure from coarsest to finest pyramid images constructed from the images in order. Matching points are then extracted considering the spatial distance and gradient orientation of linear features extracted from each image. To increase the reliability of the registration result, a quadtree-based structure is constructed (1) to mask out regions from the similarity measurement such as dense urban or heterogeneous areas, which can cause large differences in geometric and radiometric properties in two images; and (2) to extract evenly distributed and precise matching points by considering regional properties of the study site. To evaluate the proposed method’s generalization, various VHR optical and SAR sensors are used and compounded to construct the study sites. Evenly distributed matching points across the whole image were extracted, and reliable registration results by a non-linear transformation constructed from the points were derived from the proposed method.