Robust Building-Based Registration of Airborne Lidar Data and Optical Imagery on Urban Scenes

Abstract

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 between these datasets may fail to fully profit from the potential of both sensors. In this regard, 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 considering the context where data are collected at different times, from different platforms, under different acquisition configurations. This paper presents a coarse-to-fine registration method of aerial or satellite optical imagery with airborne LiDAR data acquired in such context. Firstly, a coarse registration involves processes of extraction and matching of building candidates from LiDAR data and optical imagery. Then, a Mutual Information-based fine registration is carried out. It involves a super-resolution 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 the aforementioned difficult context. For instance, considering the experimented airborne LiDAR (2011) and orthorectified aerial imagery (2016) datasets, their spatial shift is reduced by almost a half (i.e. 48.15%) after the proposed coarse registration. Moreover, the incompatibility of size and spatial resolution is well addressed by the mentioned super-resolution approach. 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 checkpair-line assessment. Promising results yielded by this registration enable further research for a complete versatile fusion methodology between airborne LiDAR and optical imagery data in this challenging context.

Unsupervised Automatic Building Extraction Using Active Contour Model on Unregistered Optical Imagery and Airborne Lidar Data

“…Such a context yields spatial discrepancies between data sets that provide the snake model with wrong initial points. Therefore, a registration has been carried out beforehand (Nguyen et al, 2019). Indeed, such a registration is substantially important to a building extraction procedure, since it involves many problems exemplified by the work of (Gilani et al, 2016).…”

Super-Resolution-Based Snake Model—An Unsupervised Method for Large-Scale Building Extraction Using Airborne LiDAR Data and Optical Image

“…As the two data sources are used jointly, a registration is necessary in order to avoid misalignment problems. This registration can be carried out a priori (i.e., data acquisition using the same platform) or a posteriori [47,48]. It aims to estimate the transformation model, allowing reducing the misalignment between the two datasets.…”

The Lidargrammetric Model Deformation Method for Altimetric UAV-ALS Data Enhancement

“…Park et al presented a solution based on a deep convolutional neural network (CNN) and the fusion of LiDAR data and dense depth information of stereo images [54]. Nguyen et al determined the relative position of both datasets by graph transformation matching (GTM) of a 3D building segmented from lidar data and 2D building segments of image data [55]. Li et al first used Structure from Motion (SfM) (IMU-and GNSS-aided) of UAV images and then iteratively minimised the differences between the depth maps derived from SfM and the raw lidar data [40].…”