Change Detection and Deformation Analysis in Point Clouds

The paper outlines a method to compare two digital surfaces of the same rock face to detect major changes resulting from detached rocks and deformations. A terrestrial laser scanning survey is used for data gathering. After georeferencing, if the cliff has a complex morphology, a 3D segmentation algorithm is applied to split the whole rock surface into more subregions with an almost planar structure. In each subregion the raw point cloud is resampled on a regular grid and multitemporal differences are analyzed. Anomalies in differences, which should be very close to zero if no geometric variations have occurred, are identified with the following purposes: (a) localizing gross changes due to rock detachments, (b) removing global rigid-body displacements, and (c) understanding local cliff deformations. In the case where the rock face is covered by vegetation, this has to be filtered out, e.g., by visual inspection of RGB images co-registered to the point cloud. This paper also describes a procedure to carry out vegetation filtering in automatic way from the analysis of near-infrared images captured by a camera integrated to laser scanner. The application of the full processing pipeline has been tested on a real case study located in the Italian pre-alpine area. Here, after filtering some vegetation, a total rock fall volume of 0.15 m3 was detected on a cliff of about 375 m2 and within a period of six months.