Multimodal Registration of Optical Images and Digital Elevation Models Using Terrain Features

Registration of multimodal images, such as optical data and digital elevation models (DEMs), is a challenging task due to significant nonlinear differences between these images. To address the problem, this letter proposes a new robust approach to integrate optical images and DEMs using terrain features. To detect these features, a simulated image is generated based on the DEM by illuminating it in the geometry of the optical image acquisition, such that typical textures induced by topography are well present as those in the optical image. Hence, the multimodal registration is performed on the optical data and the simulated image to maximize the accuracy of feature matching. The affine scale-invariant feature transform (ASIFT) is used to detect keypoints from the two images. Correspondences are matched through the nearest neighbor distance (NND) ratio, and outliers are removed using a two-step elimination procedure. The proposed method has been tested on five pairs of optical-DEM images with various spatial resolutions. Experimental results have shown that this method can provide robust registration for optical-DEM images with high accuracies of subpixel level. The novelty of proposed method is to make use of terrain features for registration, providing a new perspective for the integration of multimodal images.