Study On Filtering Algorithm Of Airborne LiDAR Point Cloud Data

Airborne Li DAR is an active remote sensing technology to determine the distance between the sensor and the target through the laser emitted by the sensor,so as to further obtain the three-dimensional information of the target efficiently and accurately,with the characteristics of all-weather uninterrupted,independent of the terrain environment,etc.It is widely concerned in the fields of digital ground model generation and forest parameter extraction,etc.It has a high social demand value.In Li DAR point cloud data processing research,point cloud filtering is a very critical step,and accurate filtering results are crucial to the accuracy and precision of subsequent data processing such as DEM generation,thus becoming one of the important technical issues in Li DAR research today.Based on the comparative analysis of existing filtering algorithms,this paper improves the existing algorithm in view of the poor filtering effect of airborne Li DAR point cloud data and the inability to retain terrain details,and strives to achieve the goal of improving the filtering accuracy of the algorithm under complex terrain conditions and better preserving significant terrain characteristics.The main work and results of the paper are as follows:1.Several mainstream point cloud filtering algorithms are analyzed and compared,and the characteristics and applicability of the algorithms are derived through experiments.For the current mainstream point cloud filtering algorithms,the principles and characteristics of the filtering algorithms are firstly analyzed and summarized.Next,the evaluation methods of filtering accuracy of filtering algorithms are summarized,and then three filtering algorithms are selected for experiments and analysis.The experimental results show that these three filtering algorithms perform better in areas with flat terrain and clear distinction between buildings and ground edges,but poorly in areas with drastic terrain changes.2.To address the problems of morphological filtering algorithms,a multi-stage morphological filtering algorithm based on thin-plate spline interpolation is proposed.Morphological point cloud filtering algorithm is the mainstream airborne Li DAR filtering algorithm,based on the analysis of the shortcomings of the progressive morphological point cloud filtering algorithm,according to the thin-plate spline interpolation can better simulate the terrain,with smooth,continuous and other characteristics,the thin-plate spline interpolation method is combined with the morphological filtering algorithm,A multilevel improved morphological filtering algorithm based on thin-plate spline interpolation is proposed,which combines the advantages of thin-plate spline multilevel interpolation surface fitting algorithm and morphological filtering algorithm,and the proposed algorithm is verified by experiments.It does not require regular arrangement of control points,suitable for discrete point interpolation,and can get better interpolation results in complex environments.The experimental results show that compared with the traditional morphological filtering algorithm,the algorithm in this paper has an overall significant improvement in accuracy,minimal error in areas where obvious buildings exist,good adaptability,and more terrain details are preserved.3.An improved algorithm for cloth simulation combined with optimal morphology is proposed.The cloth-based simulation filtering algorithm easily misclassifies unfiltered points when dealing with steep terrain,and is not as effective when dealing with groundfeature intersections.To address this situation,a cloth simulation filtering algorithm combined with optimized progressive morphology is proposed and validated experimentally.The algorithm first performs the initial filtering with a small window of morphological filtering combined with thin-plate spline interpolation to obtain the initial DEM,and then performs the secondary filtering with fabric simulation on the basis of the initial DEM to obtain the final ground point cloud and generate the digital elevation model.The experimental results show that the algorithm improves the overall accuracy of the filtering and also enhances the adaptability of the filtering algorithm.