Influence of co-alignment procedures on the co-registration accuracy of multi-epoch SFM points clouds

The well-established spread of Remotely Piloted Aircraft Systems (RPAS) as high-performance devices in the acquisition of huge datasets has found a fertile field in the geomorphological change detection in coastal areas. The ability to retrieve image datasets with multi-epoch frequency makes them effectively incisive for planning ongoing monitoring. Considering the wide accessibility to multiple Structure-from-Motion (SfM)-3D point clouds, it follows the need for their proper management to identify a profitable co-registration approach valid for a proper comparison among them. In most cases the co-registration is inherited from the same georeferencing; in other cases, it can be done manually. Unfortunately, these methodologies are time consuming and often do not properly consider geometric errors on the models. The purpose of this research work was therefore to analyse an alternative method such as the co-alignment of sparse point clouds. Given the independently or co-aligned processed multi-epoch datasets, mean errors (ME) and root-mean-square error (RMSE) on Check Points (CPs) were evaluated by adopting different georeferencing strategies. Lastly, by first generating dense point clouds and from these the Digital Elevation Models (DEMs), scalar fields regarding DEM of Differences (DoD) were computed and allowed to localize any uncertainties δz among the estimated elevations. A cloud-to-cloud comparison was obtained using the M3C2 algorithm to extrapolate systematic georeferencing errors and the local deviation between models, an evidence of how the method can affect the detectable changes. The co-alignment methodology showed encouraging results proving to be a valid alternative to more traditional approaches.