Integrating terrain knowledge into point cloud simplification for terrain modelling

Terrain models are widely used to depict the shape of the Earth's surface. With the development of photogrammetric methods, point cloud data have become one of the most popular data sources for terrain modelling. However, the obtained point clouds are of high density, which often increases redundancy rather than improving accuracy. Therefore, point cloud simplification should be a core component of terrain modelling. This paper proposes a point cloud simplification method by integrating terrain knowledge into terrain modelling (TKPCS). The method contains two steps: (1) terrain knowledge intuition and construction and (2) point cloud simplification using this terrain knowledge for terrain modelling. The proposed approach is benchmarked against improved versions of existing methods to validate its capability and accuracy in digital elevation model construction and terrain derivative extraction. The results show that the simplified points of the TKPCS method can generate finer resolution terrain models with higher accuracy and greater information entropy. The good performance of the TKPCS method is also stable at different scales. This work endeavors to transform perceptive terrain knowledge into a process of point cloud simplification and can benefit future research related to terrain modelling.
This work was supported by the National Science Foundation of China under Grant [41930102]; National Key Research and Development Program of China under Grant [2021YFB3900901]; Priority Academic Program Development of Jiangsu Higher Education Institutions under Grant [164320H116].