Optimization Classification Research Based on Laser Scanning Point Cloud Reflectance Intensity Correction

: The laser intensity information of 3D terrestrial laser scanning point cloud is very important for target classification, but the effect of classification is not ideal because of the influence of reflection material, incident angle and distance. In this paper, the intensity of point cloud is obtained by scanning the experimental wall surface with a 3D terrestrial laser scanning. By using the method of comparative analysis, the normal vector of point cloud is estimated by using k-nearest neighbor points on the wall surface for each area, and the laser scanning incidence angle is calculated. The influence of reflection material, incident angle and distance on laser intensity is analyzed by regression. Through the polynomial regression analysis of scanning distance and laser intensity, the optimized parameters are obtained to correct the laser intensity, and the corrected laser intensity is used to classify the point cloud. The results show that the point cloud laser intensity can be corrected according to the polynomial regression analysis of laser ranging data, and the corrected point cloud laser intensity has a good classification effect.