Research on Combined Filtering Algorithm of Airborne LiDAR Point Cloud Data
In order to solve the problems of poor filtering effect and unobvious terrain feature reservation when progressive morphologi-cal filtering algorithm is used to filter airborne LiDAR point cloud, this paper proposes a post-processing filtering algorithm to improve the irregular triangular network (TIN), and constructs a combined airborne LiDAR point cloud filtering algorithm. The combined algo-rithm effectively combines the advantages of the progressive morphological filtering algorithm and the improved TIN filtering algorithm. First, the progressive morphological filtering algorithm is used to process the original airborne LiDAR point cloud data and extract the initial ground points; Secondly, the traditional TIN filtering algorithm is optimized. TIN is constructed from initial ground points and seed points, and refined ground points are obtained through continuous iterative extraction. In order to verify the reliability and superi-ority of the filtering algorithm proposed in this paper, two groups of airborne LiDAR point cloud data from a certain place in Ningbo are selected for experiments. The results show that the improved filtering algorithm has lower class Ⅰ error, class Ⅱ error and total error of extracting ground points than the independent progressive morphological filtering algorithm and TIN filtering algorithm, and is not lim-ited by terrain conditions, with higher adaptability. The reliability and superiority of the improved filtering algorithm proposed in this paper are verified.
progressive morphological filtering algorithmtriangular irregular network filtering algorithmairborne LiDAR point clouddigital elevation model
万方数据
维普
