Application of Gaussian Mixture Clustering to moving surface fitting filter classification

In order to improve the accuracy and adaptability of the LiDAR point cloud filtering algorithm, an improved moving surface filtering algorithm had proposed. The boundary points of the grid were used to construct the surface constraint conditions to test whether all the building points in the grid. The area fitting was used to solve the terrain fluctuations. The Gaussian Mixture Model (GMM) in machine learning was introduced to filter and classify the terrain undulations, and the seed points in the moving surface were used as the target points in the clustering algorithm to participate in the classification learning. The experimental data was the self-test area of radar flight. The filtering effect of the self-test area was tested and judged with random sampling. At the same time, the Kappa coefficient was added as the test method to test the accuracy of the GMM algorithm on the basis of the three types of error test methods. Compared with the pedigree clustering classification algorithm, it is proved that the proposed algorithm can achieve better filtering effect.