A Phase-Congruency-Based Scene Abstraction Approach for 2D-3D Registration of Aerial Optical and LiDAR Images

Abstract

Registration of aerial images to enrich 3-D light detection and ranging (LiDAR) points with radiometric information can enhance the capability of object detection, scene classification, and semantic segmentation. However, airborne LiDAR data may not always come with on-board optical images collected during the same flight mission. Indirect georeferencing can be adopted, if ancillary imagery data are found available. Nevertheless, automatic recognition of control primitives in LiDAR and imagery datasets becomes challenging, especially when they are collected on different dates. This article proposes a generic registration mechanism based on using the phase congruency (PC) model and scene abstraction to overcome the stated challenges. The approach relies on the use of a PC measure to compute the image moments that determine the study scene's edges. Potential candidate points can be identified based on thresholding the image moments' values. A shape context descriptor is adopted to automatically pair symmetric candidate points to produce a final set of control points. Coordinate transformation parameters between the two datasets were estimated using a least squares adjustment for four registration models: first- (affine), second-, third-order polynomials, and direct linear transform models. Datasets covering different urban landscapes were used to examine the proposed workflow. The root-mean-square error of the registration is between one and two pixels. The proposed workflow is found to be computationally efficient especially with small-sized datasets, and generic enough to be applied in registering various imagery data and LiDAR point clouds.