Abstract
Point cloud filtering is a crucial step in most airborne light detection and ranging (LiDAR) applications. Many filtering algorithms have been proposed, but the filtering effect has some limitations in complex environments. To improve the filtering effect in complex terrain, a multilevel adaptive filter (MAF) combining morphological reconstruction and thin plate spline (TPS) interpolation is proposed. The digital elevation model (DEM) generated in each iteration is used as the marker image for morphological reconstruction to extract ground pixels, and an adaptive residual threshold is achieved by using terrain gradient as a compensation. The benchmark dataset provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) and another LiDAR dataset in northwestern China were used to evaluate the filtering performance of MAF. For the ISPRS benchmark dataset, MAF obtained the lowest average total error (3.72%) and highest average kappa coefficient (87.16%) compared with eight classic filtering algorithms. For the dataset in northwestern China, the DEM generated from the filtering result of MAF obtained higher accuracy than the filtering result of TerraScan. Overall, the MAF achieved promising results without considering the selection of filtering window, which may enhance the robustness and applicability of the algorithm in different environments.
Highlights
The development of airborne light detection and ranging (LiDAR) provides a new method for obtaining high-resolution geospatial information
The airborne LiDAR system is mainly composed of global positioning systems (GPSs), laser scanners (LSs), and inertial navigation systems (INSs)
The results are consistent with the conclusion that filtering algorithms usually perform well in fairly flat terrain, but the performance tends to be less stable as the slope of the terrain increases [37]
Summary
The development of airborne light detection and ranging (LiDAR) provides a new method for obtaining high-resolution geospatial information. The system can acquire three-dimensional (3D) information quickly and accurately by actively emitting laser pulses to the ground. LiDAR data have been widely used in many fields, such as digital elevation model (DEM) acquisition [1,2], road extraction [3], and forest structural parameter retrieval [4,5,6,7]. In most airborne LiDAR applications, a critical step is point cloud filtering. The filtering result directly affects the quality of the digital elevation model (DEM), which is one of the key factors affecting subsequent processing. Obtaining ground points quickly, efficiently, and accurately has always been an important issue. According to the different theoretical backgrounds, these algorithms can be divided into three categories: slope-based [12,13], morphology-based [14,15,16,17], and interpolation-based filtering algorithms [18,19,20,21]
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call