Terrain modeling from lidar data: Hierarchical K-means filtering and Markovian regularization

Lidar 3D point cloud corresponds to the terrestrial topography, including true ground and objects belonging either to vegetated areas or to human made features. This paper deals with DTM (digital terrain model) production. First step filtering data into ground and off-ground points is based on a multi-resolution coarse-to-fine approach. The K-means algorithm is used in a hierarchical way that provides robust data filtering. The number of cluster splits is used to automatically qualify the filtering reliability. This point is rarely treated in previous works. Secondly, a regularization process over ground points generates an accurate DTM on a regular grid. The fine DTM is processed with ground points without using classical interpolation algorithms. In fact, a Markovian regularization minimizes a global energy that confronts the terrain regularity and the goodness of fit to the data. It also depends on the filtering reliability. Conclusive results are presented on vegetated and mountainous areas and provide realistic terrain models.