LIDAR data filtering and classification with TIN and assistant plane

Abstract

LIDAR is a new promising technique in obtaining instantly 3D point cloud data representing the earth surface information. In order to extract valuable earth surface feature information for further application, 3D sub-randomly spatial distributed LIDAR point cloud should be filtered and classified firstly. In this article, a new LIDAR data filtering and classification algorithm is presented. First, the points' neighboring relation and height-jump situation in TIN (triangulated irregular network) model for 3D LIDAR point cloud are analyzed. After that, the filtering algorithm based on TIN neighboring relation and height-jump is presented. Third, an assistant plane is designed in TIN neighborhood filtering algorithm in order to yield more effective filtering result. Then, the LIDAR points are classified into bare ground points, building points and vegetation points using the above filtering algorithms. The experiment is performed using the airborne LIDAR data, and the result shows that this method has better effect on filtering and classification of LIDAR point cloud data.

Dynamic Slicing and Reconstruction Algorithm for Precise Canopy Volume Estimation in 3D Citrus Tree Point Clouds

“…To facilitate the subsequent extraction of fruit tree information, it is necessary to select a suitable algorithm to filter the ground points in the test area. Common algorithms include least squares plane fitting (LSPF) [42], random sample consensus (RANSAC) [43], and the cloth simulation framework (CSF) [44]. The LSPF fits the ground plane model by minimizing the distance between the point and the plane.…”