LiDAR point cloud classification using point elevation and reflection intensity

The data obtained from LiDAR includes a lot of valuable information and is applied in many different fields such as geodesy - cartography, and antiques. information transmission, etc. LiDAR point cloud contains a lot of information about the object such as high point, point reflection intensity, nominal distance (NPS), and grayscale value, etc., each information is used in different problems. to clarify the three-dimensional spatial distribution, the zoning surface, or the features of the topography and features in the survey area. In the article, the authors use information altitude and reflection intensity, two typical symbols of data LiDAR, to implement a mathematical layer application to set digital elevation (DEM), model the face number (DSM), and 3D model to verify the partition of address, address at the area of testing. Pitch information is used by the author to separate groups of ground (ground) and non-ground (non-ground) points. Value reflection will be used to enhance accuracy when performing groundless classification into vegetative strata, and tall buildings. The use of point intensity reflection enhances the accuracy of previous high point-based geometry processing methods. With the accuracy of the problem analysis class reaching (ground (93.8%), building (91%), and vegetation (93,7%)), the models are set up just out of the partition of the required answer surface of the problem application.