Multimodal Registration of Optical Images and Digital Elevation Models Using Terrain Features
Jiayang Zhao, Hui Li · IEEE Geoscience and Remote Sensing Letters · 2022
Registration of multimodal images such as optical data and digital elevation models (DEM) is a challenging task due to significant non-linear differences between these images. To address the problem, this paper proposes a new robust approach to integrate optical images and DEMs by using terrain features. In order 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 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 sub pixel 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.