2019/09/30 by Thanh Huy Nguyen, Sylvie Daniel, Didier Guériot +5 · 13 citations
Computer Science · Earth and Planetary Sciences · Engineering · Environmental Science · #3D Surveying and Cultural Heritage #Artificial intelligence #Computer science #Computer vision #Context (archaeology) #Geography #Image (mathematics) #Image registration #Image resolution #Lidar #Orthophoto #Ranging #Remote Sensing and LiDAR Applications #Remote sensing #Robotics and Sensor-Based Localization #Satellite imagery #Sensor fusion #cs.CV #eess.IV
paper · pdf · doi:10.1109/jstars.2020.2987305
published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, 3125-3144 (Institute of Electrical and Electronics Engineers) · 20 pages, 15 figures. Accepted to be published in IEEE JSTARS
openalex publication_date 2020/01/01 · arxiv created 2020/04/15 · arxiv updated 2020/06/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Applications based on synergistic integration of optical imagery and LiDAR data are receiving a growing interest from the remote sensing community. However, a misaligned integration of these datasets fails to fully profit from the potential of both sensors. An optimum fusion of optical imagery and LiDAR data requires an accurate registration. This is a complex problem since a versatile solution is still missing, especially when data are collected at different times, from different platforms, under different acquisition configurations. This article presents a coarse-to-fine registration method of optical imagery with airborne LiDAR data acquired in such context. First, a coarse registration involves processes of extraction and matching of building candidates from the two datasets. Then, a mutual-information-based fine registration is carried out. It involves a superresolution approach applied to LiDAR data to generate images with the same resolution as the optical image, and a local approach of transformation model estimation. The proposed method succeeds at overcoming the challenges associated with this difficult context. For instance, considering the experimented airborne LiDAR (2011) and orthorectified aerial imagery (2016) datasets, their spatial shift is reduced by 48.15% after the proposed coarse registration. Moreover, the incompatibility of size and spatial resolution is well addressed by the superresolution. Finally, a high accuracy of dataset alignment is also achieved, highlighted by a 40-cm error based on a check-point assessment and a 64-cm error based on a check-pair-line assessment. These promising results enable further researches for a complete fusion methodology between these datasets in this challenging context.