Autocorrelation Statistics-Based Algorithms for Automatic Ground and Non-Ground Classification of Lidar Data

Classification of lidar data to ground and nonground points is important for accurate topography mapping and reliable estimation of slope, volume and buildings’ geometry over urban areas. Manual or semi-automatic classification provides relatively good results, however, automatic classification in complex areas with diverse object sizes is still challenging. This research aims to propose two novel algorithms based on Getis-Ord Gi* (or Gi* for short) and Local Moran’s I (LMI) statistics to classify a lidar point cloud into a set of points representing ground and another set of points reflected from non-ground e.g. buildings and vegetation. The Two statistics, Gi* and LMI, have been widely used in cluster analysis to identify clustered features of high z-scores and low z-scores. Based on the two statistics, we proposed two classification algorithms that allow varying window sizes e.g. 100 m, 150 m and 200 m, and applied the algorithms to the lidar data in order to obtain optimal classification results. The results show that the Gi*-based algorithm decreases omission errors but increases commission errors when compared to the LMI-based approach. Overall the 100-m window size outperforms than the other window sizes in terms of feature extraction in slan areas, whereas the 150-m window size provides slightly better results in a complex scene of high-rise buildings and dense vegetation, and the 200-m window size is more efficient if large buildings are present in the study area. This feasibility study indicates that autocorrelation statistics such as Gi* and LMI can be effectively used to classify a lidar point cloud.