Study On Airborne LiDAR Point Cloud Data Filtering Method

As a new technology of acquiring three-dimensional geographic information, the Airborne Li DAR(Light Detection And Ranging) technology acquires a number of point cloud data. The processing and application for these point cloud data have been the research of many domestic or foreign scholars in recent years. The technology of Li DAR point cloud data filtering is a critical step of the point cloud data processing, and DEM(Digital Elevation Model)production technology is the basic data processing. For the difficulties in the filtering process and the development of filtering technology, this thesis aims at improving and proposing filtering algorithms to improve the performance of the filtering algorithms and generate the accurate DEM. The main work and innovation of this thesis are:1. This thesis illustrates the theories and methods of the airborne Li DAR data filtering, introduces the concept and characteristics of Li DAR data, and analyzes the various airborne Li DAR filtering techniques.2. This thesis analyzes a variety of airborne Li DAR point cloud data filtering algorithms, introduces the general steps of the airborne Li DAR filtering algorithm, and gives a set of reference data and the definition of error which ISPRS(International Society for Photogrammetry and Remote Sensing) III put forward.3. Based on studying the mathematic morphological filter, a LIDAR point cloud data filtering algorithm which is based on region prediction and Mathematic morphological filter is proposed. The method firstly creates a regular grid with point cloud data and removes outliers, then divides the experimental area into different blocks and uses sub-blocks’ elevation standard deviation to predict the terrain slope parameter, finally applies the progressive morphological filtering and determines ground point. This algorithm has an advantage of obtaining threshold adaptively by the conditions of topographic relief of the region. The experimental results show that the method can keep the ground points, effectively remove non-ground points, and minimize total error rates.4. This thesis introduces the triangulation net and the point cloud filtering technology based on the triangulation net, and after studies region growth, proposes a TIN(Triangulated Irregular Network) filtering algorithm based on region growth. The method firstly removes outliers and builds TIN, secondly utilize the elevation information of the adjacent triangles of each triangle to detect the building edge points and after region growing acquires the points of building roofs, then detects vegetation points with the morphological filtering algorithm, finally determines the ground point set and generates DEM. The experimental results show that the method can effectively remove non-ground points, keep the ground points and is particularly effective at minimizing total error rates while maintaining acceptable Type I and Type II error rates.