A Kind Of Airborne Lidar Date Multi-level Filtering Method Base On The Principles Of Height Jump

As a new means, Airborne LiDAR System has made a seminal breakthrough in the field of access to information in three-dimensional space. Compared with the traditional Photogrammetry and Remote Sensing, this technology can be less impacted by the weather, light and other natural conditions, and it can acquire three-dimensional data all day long. Data processing is the key point of the technology, and it takes about80percent of the total working time. Acquiring DEM by filtering point cloud data is the first step of the date processing. The quality of filtering will affect the quality of the subsequent products and the result of the application of this technology in production directly, which is the most important and difficult part in the study of the LiDAR technology. Therefore, the acquisition of DEM by filtering of LiDAR point cloud data is a significant research topic currently. This article has improved and tested the filtering methods of point cloud based on the related research. What’s more, it elaborates the existing key problems and details of filtering methods. The details are as follows:(1) This article introduces the development of the LiDAR system. It elaborates the form, format, characteristics and the organization methods of LiDAR point cloud data, which provides guarantee for the subsequent point cloud data filtering algorithm.(2) This article analyzes the filtering method of airborne LiDAR point cloud data at home and abroad, and introduces several classical algorithms. Several improved filtering algorithms are systematically described, and the shortcomings are summarized in this paper, providing a theoretical basis for further research.(3) Improved TIN filtering based on height jump. The point cloud is classified by the Mean Shift. Then it can be block processed by the classification results. Different threshold is set based on the characteristics of each block. Meanwhile, the space angle is joined as a threshold parameter on the basis of the original threshold to improve the accuracy of filtering results.(4) This article designs the algorithm based on hierarchical adaptive moving curved surface and fitting. This algorithm is the further deepening of the thought of the Improved TIN filtering based on height jump. According to different levels of the height jump and the height jump characteristics of different objects. it can gradually filter out the certain height objects. Firstly, in order to filter medium and high objects, this algorithm uses the improved mean-tolerance method to rough filter, which improves the proportion of ground points and efficiency of the filter. After rough filtering, the paper uses the Mean Shift to classify and organize the point cloud by the virtual grid. Finally, we design a locally adaptive threshold method to remove the remaining non-ground points by moving curved surface and fitting.Research shows that under various terrain conditions, the improved TIN filtering based on height jump in this paper, can filter out most of the ground points which the original algorithm cannot filter out. It can keep the terrain details well. The error Ⅰ, error Ⅱ or the total error are all significantly reduced in the improved algorithm according to the quantitative analysis of the filtering result. The adaptive threshold is set in the improved mean-tolerance in this paper, which can better remove most of the objects, reducing the amount of point cloud data, improving the efficiency of the second filtering. The hierarchical adaptive moving curved surface and fitting has strong adaptability, and it can effectively solve the problems, such as low outliers, error transfer and accumulation and Terrain excessive corrosion etc., and it will get better filtering results.