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3D point clouds of natural environments relevant to problems in geomorphologyoften require classification of the data into elementary relevant classes. Atypical example is the separation of riparian vegetation from ground in fluvialenvironments, the distinction between fresh surfaces and rockfall in cliffenvironments, or more generally the classification of surfaces according totheir morphology. Natural surfaces are heterogeneous and their distinctiveproperties are seldom defined at a unique scale, prompting the use ofmulti-scale criteria to achieve a high degree of classification success. Wehave thus defined a multi-scale measure of the point cloud dimensionalityaround each point, which characterizes the local 3D organization. We can thusmonitor how the local cloud geometry behaves across scales. We present thetechnique and illustrate its efficiency in separating riparian vegetation fromground and classifying a mountain stream as vegetation, rock, gravel or watersurface. In these two cases, separating the vegetation from ground or otherclasses achieve accuracy larger than 98 %. Comparison with a single scaleapproach shows the superiority of the multi-scale analysis in enhancing classseparability and spatial resolution. The technique is robust to missing data,shadow zones and changes in point density within the scene. The classificationis fast and accurate and can account for some degree of intra-classmorphological variability such as different vegetation types. A probabilisticconfidence in the classification result is given at each point, allowing theuser to remove the points for which the classification is uncertain. Theprocess can be both fully automated, but also fully customized by the userincluding a graphical definition of the classifiers. Although developed forfully 3D data, the method can be readily applied to 2.5D airborne lidar data.

3D Terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: applications in geomorphology