Adaptive coarse-to-fine clustering and terrain feature-aware-based method for reducing LiDAR terrain point clouds

Light detection and ranging (LiDAR)-derived point cloud has become the standard spatial data for digital terrain model (DTM) construction; however, it suffers from huge data with much redundant information due to the oversampling. This often causes considerable inconvenience in the downstream data processing. To this end, an adaptive coarse-to-fine clustering and terrain feature-aware-based method is proposed to reduce data points in the context of terrain modeling in this paper. Firstly, a coarse-to-fine clustering method with the consideration of terrain complexity is developed to adaptively cluster LiDAR terrain points. Then, according to the geometric properties of terrain breaklines, a terrain feature-aware multi-strategy method is presented to pick representative points in the clusters. Finally, important boundary points including inflection point on the boundary curve and critical point on terrain features are further selected. The proposed method is compared with seven state-of-the-art point cloud simplification methods under six data reduction ratios on six plots with different terrain characteristics. Results indicate that the proposed method obtains a good balance between terrain-feature preservation and uniform distribution of data points. Compared to the state-of-the-art methods, the proposed method reduces the average root mean square errors (absolute errors) of the DTMs by 12.2%-51.7% (7.69%-83.8%) on the six plots. Moreover, the proposed method obtains the mean terrain slope and terrain roughness more reasonably approximate to the references. In short, the newly developed method can be considered as an alternative tool to select representative points from the huge remote-sensing-derived point cloud in the context of DTM production.