A data-driven morphological filtering algorithm for digital terrain model generation from airborne LiDAR data

Ground filtering algorithms (GFs) are widely used in point cloud processing to generate digital terrain models. Existing GFs typically rely on rule-based or machine learning approaches to separate ground and non-ground points within an airborne point cloud. However, they often struggle to accurately extract ground points in scenarios containing mountains and heterogeneous buildings. To enhance the accuracy and robustness of ground filtering for airborne point clouds, we propose a data-driven morphological filtering algorithm (DMF). DMF begins by identifying near-ground voxel centroids after voxelizing the input point clouds. Next, a digital elevation model is constructed based on the elevation information of these near-ground voxel centroids. A composite morphological filter is then designed to identify ground and non-ground patches within the digital elevation model before labeling their inner near-ground voxel centroids as GF-support nodes. The composite morphological filter is used to recognize non-ground areas with incomplete edge structures depicted in the input point cloud and to correct misclassified areas. Finally, a bidirectional k-dimensional tree search engine is built between the GF-support nodes and the input point cloud to separate ground and non-ground points. Experimental results show that DMF achieves ground filtering accuracy with an average F-score greater than 0.88, demonstrating robustness in generating digital terrain models across various test scenarios. Furthermore, the intermediate outputs of DMF enable instance segmentation of artificial objects in airborne point clouds. The code for DMF will be shared on GitHub (https://github.com/wbx1727031/DMF).