| Airborne Lidar laser radar data processing is an important part of the application system, about 80% of the workload is concentrated in it, including data filtering DEM production technology is the most basic data processing work, its follow-up of digital products, Application security plays a key role, but also Lidar technology development and cutting-edge research topics. This paper focuses on the airborne Lidar data filtering and sorting technology, combined with point cloud data structure, in-depth study and exploration. The main work and innovations are:1. A systematic exposition of the principle of Airborne Lidar and the positioning of the composition, described Lidar data structure and surface features the main characteristics of data processing difficulties, the practical application and follow-up of the filtering algorithm to provide a theoretical basis.2. Analysis of Lidar point cloud data in the original noise causes the formation of rough almost to introduce the formation of new equipment, the noise characteristics of the data, the threshold elevation for the traditional method requires a lot of statistical analysis and experiments to determine the shortcomings, presents a fitting plane and elevation near the point of combining the density criterion, the iterative calculations - adaptive changes in the way of detection threshold parameter noise points.Experimental results show that the method can automatically detect outliers purpose of noise points, significantly lower risk of miscarriage of justice.3. Proposed structure based on virtual grid to improve morphological filter. The data using a virtual grid organization, to avoid interpolating the original data grid loss of accuracy. Morphological gradient as a data point is the opening operation of the threshold condition, improve the scarp terrain to determine the accuracy of the mutation, two consecutive morphological opening operation on the results of a second judge, using the edge of the regional characteristics of surface featuresObject Recognition as a feature, to improve the accuracy of filtering to avoid open operation blind misjudgment of the terrain data. Experimental analysis shows that the algorithm has strong adaptability to the natural complex in non-urban terrain with good filtering effect.4. Is proposed based on octree data structure of the point cloud cluster segmentation.Use of octree data structure to complete point cloud data clustering segmentation, using segmentation octree nodes - two-step integration of fragmented regional integration, improved segmentation results, to improve accuracy. At the same time in the establishment of octree nodes to complete the process of indexing the original data structure, between sets of data points to facilitate the mathematical calculations, to improve filtering efficiency.5. Segmentation based on the above results, we propose a new topology information and the regional height discontinuity filter. Segmentation results of the regional data for the design of a topology information, block data set types in the region to determine, considering the height difference between regions and between the discontinuity between the topology of the dual criteria, the use of iterative calculation method gradually removing the surface features of regional data sets in order to eventually achieve the purpose of filtering. This method solves the surface features of traditional clustering filter to determine shortcomings in urban terrain data with good performance, reliable and accurate filtering results.6. Presents a waveform width threshold of full-waveform data filtering methods. Full waveform data for the characteristics of a mathematical proof of its derivation is the superposition of Gaussian wave model using the least square iterative optimization method to estimate wave parameters, the use of waveform width points and terrain points as vegetation classification threshold, complete vegetation zone data point filtering.
|
PDF Full Text Request