Automatic tree stem detection – a geometric feature based approach for MLS point clouds

Recognition of tree stem is a fundamental task for obtaining various geometric attributes of trees such as diameter, height, stem position and so on for diverse of urban application. We propose a novel tree stem segmentation approach using geometric features corresponding to trees for high density MLS point data covering in urban environments. The principal direction and shape of point subsets are used as geometric features. Point orientation exhibits the most variance (shape of point set) of a point neighbourhood, assists to measure similarity, while shape provides the dimensional information of a group of points. Points residing on a stem can be isolated by defining various rules based on these geometric features. The shape characterization step is accomplished by estimating the structure tensor with principal component analysis. These features are assigned to different steps of our segmentation algorithm. Wrong segmentations mainly occur in the area where our rules have failed, such as vertical type objects, road poles and light post. To overcome these problems, global shape is further checked. The experiment is performed to evaluate the method; it shows that more than 90% of tree stems are detected. The overall accuracy of the proposed method is 90.6%. The results show that principal direction and shape analysis are sufficient for the tree stem recognition from MLS point cloud in a relatively complex urban area.