Robust Building-based Registration of Airborne LiDAR Data and Optical Imagery on Urban Scenes
This addresses the challenge of aligning multi-source urban data for applications like mapping and monitoring, but it is incremental as it builds on existing segmentation and matching techniques.
The paper tackles the problem of registering airborne LiDAR data and optical imagery from different sources by proposing a robust method based on building regions, using Graph Transformation Matching to reduce relative shifts and enable high-quality data fusion.
The motivation of this paper is to address the problem of registering airborne LiDAR data and optical aerial or satellite imagery acquired from different platforms, at different times, with different points of view and levels of detail. In this paper, we present a robust registration method based on building regions, which are extracted from optical images using mean shift segmentation, and from LiDAR data using a 3D point cloud filtering process. The matching of the extracted building segments is then carried out using Graph Transformation Matching (GTM) which allows to determine a common pattern of relative positions of segment centers. Thanks to this registration, the relative shifts between the data sets are significantly reduced, which enables a subsequent fine registration and a resulting high-quality data fusion.