Presenting a Morphological Based Approach for Filtering The Point Cloud to Extract the Digital Terrain Model

The Digital terrain model is an important geospatial product used as the basis of many practical projects related to geospatial information. Nowadays, a dense point cloud can be generated using the LiDAR data. Actually, the acquired point cloud of the LiDAR, presents a digital surface model that contains ground and non-ground objects. The purpose of this paper is to present a new approach of extracting the digital terrain model from the digital surface model. In the first step, noises were removed by preprocessing; then the irregular point cloud was converted to raster data. In the next step, the proposed gradual geodesic dilation and labeling approaches scan were applied in order to detect and eliminate the non-ground objects. The basis of gradual geodesic dilation approach was to increase the structural element size in each step, investigate the height heterogeneity and remove the non-ground objects, gradually. Also, utilizing the innovative scan labeling approach which operated based on slope differential helped to remove the non-ground objects completely. Finally, the non-ground objects were removed and the lost regions were retrieved and the digital terrain model was generated by interpolation. For analyzing the proposed approach, the reference data of the ISPRS was employed. The analyzing results in the five test areas indicated 4.61%, 6.97% and 3.17% for Type I, Type II and total errors, respectively. These results clarify the good performance of the proposed approach for detecting the non-ground objects.