Abstract
Abstract. Denoising is a key pre-processing step for many airborne LiDAR point cloud applications. However, the previous algorithms have a number of problems, which affect the quality of point cloud post-processing, such as DTM generation. In this paper, a novel automated denoising algorithm is proposed based on empirical mode decomposition to remove outliers from airborne LiDAR point cloud. Comparing with traditional point cloud denoising algorithms, the proposed method can detect outliers from a signal processing perspective. Firstly, airborne LiDAR point clouds are decomposed into a series of intrinsic mode functions with the help of morphological operations, which would significantly decrease the computational complexity. By applying OTSU algorithm to these intrinsic mode functions, noise-dominant components can be detected and filtered. Finally, outliers are detected automatically by comparing observed elevations and reconstructed elevations. Three datasets located at three different cities in China were used to verify the validity and robustness of the proposed method. The experimental results demonstrate that the proposed method removes both high and low outliers effectively with various terrain features while preserving useful ground details.
Highlights
Airborne LiDAR (Light Detection and Ranging) has become an important remote sensing means for capturing the threedimensional geometry of the Earth (Lin and Zhang, 2014)
The existence of noisy points will always bring about some negative effects, including that (1) the quality of digital elevation model (DTM) generation may be affected by the noisy points, especially the low outliers, since most of the filtering algorithms always assume that the lowest points in the local areas must belong to ground; (2) the rendering of point cloud based on elevation will be influenced due to the maximal or minimal elevations of outliers; and (3) mass of noisy point will incur low three-dimensional model reconstruction quality and decrease the degree of automation
Airborne LiDAR point cloud was first transformed into digital surface model (DSM) using nearest neighbor interpolation, since raster data own the strength of high efficiency and are easy to implement
Summary
Airborne LiDAR (Light Detection and Ranging) has become an important remote sensing means for capturing the threedimensional geometry of the Earth (Lin and Zhang, 2014). By integrating the global positioning system, the inertial navigation system and the laser scanning sensor, huge amount of point clouds reflected from Earth’s surface can be obtained (Yang et al, 2016). These point clouds consist of three parts, namely ground points, object points and noisy points (Meng et al, 2010). The noisy points can be categorized into high and low outliers. The low outliers normally originate from multi-path and errors in the laser range finder and do not belong to the landscape (Sithole and Vosselman, 2003). The existence of noisy points will always bring about some negative effects, including that (1) the quality of DTM generation may be affected by the noisy points, especially the low outliers, since most of the filtering algorithms always assume that the lowest points in the local areas must belong to ground; (2) the rendering of point cloud based on elevation will be influenced due to the maximal or minimal elevations of outliers; and (3) mass of noisy point will incur low three-dimensional model reconstruction quality and decrease the degree of automation
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