Change Detection in Photogrammetric Point Clouds for Monitoring of Alpine, Gravitational Mass Movements

Abstract

Abstract. This contribution discusses the accuracy and the applicability of Photogrammetric point clouds based on dense image matching for the monitoring of gravitational mass movements caused by crevices. Four terrestrial image sequences for three different time epochs have been recorded and oriented using ground control point in a local reference frame. For the first epoch, two sequences are recorded, one in the morning and one in the afternoon to evaluate the noise level within the point clouds for a static geometry and changing light conditions. The second epoch is recorded a few months after the first epoch where also no significant change has occurred in between. The third epoch is recorded after one year with changes detected. As all point clouds are given in the same local coordinate frame and thus are co-registered via the ground control points, change detection is based on calculating the Multiscale-Model-to-Model-Cloud distances (M3C2) of the point clouds. Results show no movements for the first year, but identify significant movements comparing the third epoch taken in the second year. Besides the noise level estimation, the quality checks include the accuracy of the camera orientations based on ground control points, the covariances of the bundle adjustment, and a comparison the Geodetic measurements of additional control points and a laser scanning point cloud of a part of the crevice. Additionally, geological measurements of the movements have been performed using extensometers.

Photogrammetric Monitoring of Gravitational Mass Movements in Alpine Regions by Markerless 3d Motion Capture

“…The distances between the core points and the projected points are used as a measure of change. A more detailed description of the M3C2 distances and their use in the context of rock slope monitoring can be found in (Dinkel et al, 2020).…”

“…However, these are often sensitive to outliers and measurement noise. A common alternative is the use of Multiscale Model-to-Model Cloud (M3C2) distances (Dinkel et al, 2020, Holst et al, 2017a. Here, the distance is calculated on the basis of key points and their local normals.…”