Abstract: Aiming at the problem that UAV photogrammetry is susceptible to vegetation influence and terrain loss when establishing DEM, this paper proposes a method to establish refinement of DEM in woodland based on ground point clouds fusing images of different routes. Firstly, the images are classified according to the UAV route, and the ground point clouds are extracted separately.Then the ICP algorithm with inverse distance weight constraint is used to construct the fused ground point cloud. Finally,the woodland refinement of DEM is established based on the fused ground point cloud. The results show that the number of fused ground point clouds is 2 182 740, and the density is 9612/m². The error in DEM established based on fused ground point cloud is 7.3 cm, and the correlation coefficient with the actual terrain reaches 0.925, and in different vegetation areas, the error in the DEM established by the fused ground point cloud is within 10 cm, and the correlation coefficient with the actual terrain is above 0.89. Experiments verify the feasibility and applicability of the proposed method, and provide a reference for the establishment of fine DEM of woodland by UAV photogrammetry.

Key words: image dense point cloud, fusion of point cloud, IDW algorithm, refinement of DEM, precision analysis