Abstract
Abstract. Computer vision applications have been increasingly gaining space in the field of remote sensing and geosciences for automated terrain classification and semantic labelling purposes. The continuous and rapid development of monitoring techniques and enhancements in the spatial resolution of sensors have increased the demand for new remote sensing data analysis approaches. For semantic labelling of 2D (or 2.5D) image terrain representations for rock slopes, it has been shown that Object-Based Image Analysis (OBIA) results in high efficiency and accurate identification of landslide hazards. However, the application of such object-based approaches in 3D point cloud analysis is still under development for geospatial data analysis. In the field of engineering geology, which deals with complex rural landscapes, frequently the analysis needs to be conducted based solely on 3D geometrical information accounting for multiple scales simultaneously. In this study, the primary segmentation step of the object-based model is applied to a TLS-derived point cloud collected at a landslide-active rock slope. The 3D point cloud segmentation methodology proposed here builds on the principles of the Fractal Net Evolution Approach (FNEA). The objective is to provide a geometry-based point cloud segmentation framework that preserves the 3D character of the data throughout the process and favours the multi-scale analysis. The segmentation is performed on the basis of supervoxels based on purely geometrical local descriptors derived directly from the TLS point clouds and comprises the basis for the subsequent steps towards the development of an efficient Object-Based Point cloud Analysis (OBPA) framework in rock slope stability assessment by adding semantic meaning to the data through a homogenization process.
Highlights
Object-oriented classification approaches have become increasingly popular in the field of remote sensing and geosciences since the early 2000s, in parallel with the growth in GIScience (Blaschke, 2010)
Object-oriented classification approaches have already become a subject of research in 3D point cloud semantic labelling (Rutzinger et al 2008; Mayr et al 2017)
Recent studies have shown a great potential in engineering geology applications in complex terrains, the final classification result is still limited to single-scale analysis
Summary
Object-oriented classification approaches have become increasingly popular in the field of remote sensing and geosciences since the early 2000s, in parallel with the growth in GIScience (Blaschke, 2010). Image segmentation is routinely used in image processing, it has gained new momentum when incorporated in object-based analysis facilitating the calculation of additional segment properties that are used as attributes for discriminating features This primary segmentation step is usually done for one specific object scale (or model), but OBIA accommodates multiscale data handling (Blaschke, 2010). Exploit geometric signatures using low-level descriptors at userdefined neighbourhoods to label complex natural scenes using point-based machine learning classification approaches Methods such as CANUPO (Brodu and Lague, 2012), that examines the dimensionality of each point’s successively-increasing-sized neighborhood and selects the most discriminating of those, have been successfully applied to complex rock slope scenes for the classification of debris flow hazard elements (Bonneau & Hutchinson, 2019). The algorithm provides the user with the ability to investigate the geometric signature of the structures at multiple scales simultaneously, by means of the FNEA (Fractal Net Evolution Approach) (Baatz & Schape, 1999), and to benefit from the semantic information becoming available
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