Abstract
Most filtering algorithms suffer from complex parameter settings or threshold adjusting. To solve this problem, this paper proposes an improved skewness balancing filtering algorithm based on thin plate spline (TPS) interpolation. The proposed algorithm filters the nonground points in an iterative manner. A reference surface that reflects the fluctuation of the terrain is generated using the TPS interpolation method. Accordingly, the elevation difference from each point to the surface can be calculated. By applying the skewness balancing principle to these elevation differences, nonground points can be removed automatically. To verify the validity and robustness of the proposed method, the datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) were adopted. The experimental results show that this presented method can adapt to complex environments and achieve a higher filtering accuracy than the traditional skewness balancing algorithm. Moreover, in comparison with the other eight filtering methods tested by the ISPRS and four improved filtering methods proposed recently, the proposed method achieved an average total error of 5.39%, which is smaller than that of most of these other methods.
Highlights
In recent decades, the airborne light detection and ranging (LiDAR) technique has developed very quickly and become an one important means for acquiring remote sensing data [1,2]
To improve thebalancing accuracy filtering and automation ofbased the filtering method, this this paper proposed an improved skewness algorithm on thin plate spline to airborne
Improved balancing filtering algorithm of based on thin method, plate spline paper proposed an improved skewness balancing filtering algorithm based on thin plate spline paper proposed improved skewness filtering algorithm based on thin plate spline interpolation
Summary
The airborne light detection and ranging (LiDAR) technique has developed very quickly and become an one important means for acquiring remote sensing data [1,2]. LiDAR system is generally composed of a laser scanner, a global positioning system (GPS) and an inertial measurement unit (IMU) [3] With these three components cooperating with each other, a large amount of LiDAR point clouds reflected from the ground and objects (cars, trees, buildings, etc.) can be obtained. Sci. 2019, 9, 203 which is beneficial for modeling; and (3) LiDAR pulses can penetrate the tree canopy, which makes this technique convenient for detecting topography in forest areas [4,5,6,7] Owing to these advantages, airborne LiDAR has been applied to many applications, such as three-dimensional building model construction [8,9], power line inspection [10], change detection [11], and land cover classification [12]
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