Lidar-Based Dem Thinning Algorithm Under The Restriction Of Precision

AirborneLiDAR (Light Detection And Ranging) can obtain high-density point clouds and has gradually become one of the most effective way to quickly build high precision DEM (Digital Elevation Model). In the practical application, however, such a large amount of data is not always necessary. On the other hand, huge amounts of data will increase the difficulty of data processing, affecting the work efficiency greatly. Though some of the international and domestic academics have been doing a lot of research on the compression algorithm of point clouds and made a lot of remarkable achievements, how to better match terrain and keep the terrain feature points is still the key point and difficulty, meanwhile, there is no definite scale to measure the extent of compression problem of the point cloud data to a specific project. Therefore, this essay has been carried out the following contents:(1) The paper mainly summarizes the advantages and problems of the existing algorithm, with review on the domestic and foreign research status on LiDAR-based DEMreduction.(2) The paper introduces the concept of terrain information entropy and proposes a DEMcompression algorithm based on terrain information entropy as its value can serve as a good characterization of terrain complexity.(3) It centers on describing the issues related to DEM accuracy, including the concept of DEM accuracy, mathematical models, evaluation methods.evaluation index and so on.(4) It focuses on researching and summarizing the compression algorithm based on gradient, thus, with the combine of study on DEM precision, proposing a compression algorithm of digital elevation model based on airborne LiDAR point clouds data with the constraint of precision.(5) The experimental resultsindicatethat using the LiDAR-based DEMthinning algorithm under the restriction of precision can match compression ratio and the precision index well. Thus the DEM precision index has been gotten when the compression ratio data of flat, hilly, mountainous is respectively10%-90%. And the minimum compression ratio has also been obtained meeting the corresponding DEM precision request of plain, hilly, mountainous data when scales are1:500,1:1000,1:2000,1:5000and1:10000. Therefore, it has a reference value for the required degreeofDEM compression.