Lidar Data Classification Using Hierarchical K-means Clustering

This paper deals with lidar point cloud filtering and classification for modelling the Terrain and more generally for scene segmentation. In this study, we propose to use the well-known K-means clustering algorithm that filters and segments (point cloud) data. The Kmeans clustering is well adapted to lidar data processing, since different feature attributes can be used depending on the desired classes. Attributes may be geometric or textural when processing only 3D-point cloud but also spectral in case of joint use of optical images and lidar data. The algorithm is based on a fixed neighborhood size that can deal with steep relief covered by dense vegetation, mountainous area and terrains which present microrelieves. The novelty of our algorithm consists in providing a hierarchical splitting clustering to extract ground points. The number of cluster splits is used to qualify automatically the classification reliability. This point is rarely treated in previous works. Moreover landscape predictors such as slope map are used to locally refine the classification. Finally, the methodology is extended to a multiscale framework. The hierarchical clustering is processed from coarse DTM resolution to finer one. This implementation improves the algorithm robustness and ensures reliable ground estimation. Quantitative and qualitative results are presented on the ISPRS data set.