An Improved Morphological Algorithm for Filtering Airborne LiDAR Point Cloud Based on Multi-Level Kriging Interpolation

Abstract

Abstract:Filtering is one of the core post-processing steps for airborne LiDAR point cloud. In recent years, the morphology-based filtering algorithms have proven to be a powerful and efficient tool for filtering airborne LiDAR point cloud. However, most traditional morphology-based algorithms have difficulties in preserving abrupt terrain features, especially when using larger filtering windows. In order to suppress the omission error caused by protruding terrain features, this paper proposes an improved morphological algorithm based on multi-level kriging interpolation. This algorithm is essentially a combination of progressive morphological filtering algorithm and multi-level interpolation filtering algorithm. The morphological opening operation is performed with filtering window gradually downsizing, while kriging interpolation is conducted at different levels according to the different filtering windows. This process is iterative in a top to down fashion until the filtering window is no longer greater than the preset minimum filtering window. Fifteen samples provided by the ISPRS commission were chosen to test the performance of the proposed algorithm. Experimental results show that the proposed method can achieve promising results not only in flat urban areas but also in rural areas. Comparing with other eight classical filtering methods, the proposed method obtained the lowest omission error, and preserved protruding terrain features better.

Filtering Airborne LiDAR Data Through Complementary Cloth Simulation and Progressive TIN Densification Filters

“…POT was further refined with 1 • increments from POT-2 • to POT+2 • . Figure 14 shows the accuracies of the proposed algorithm under CR = 0.5 m against other 31 algorithms [2, [24][25][26][27][31][32][33][34]37,38,[41][42][43][44]47,[54][55][56][57][58][59][60][61]. The average total error of the proposed algorithm under CR = 0.5 m provides competitive performance, below four filtering algorithms, but above 27 other algorithms.…”

“…In most applications, a filtering operation for separating point clouds into ground and non-ground points is a preliminary and essential step. Many filtering algorithms have been proposed to automatically filter ground points, and these algorithms can be mainly grouped into three categories, namely, slope-based methods [17][18][19][20][21], mathematical morphology-based methods [22][23][24][25][26][27], and surface-based methods [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44]. An experimental comparison of eight filtering algorithms was performed by Sithole and Vosselman [2].…”

A Novel Denoising Algorithm for Airborne Lidar Point Cloud Based on Empirical Mode Decomposition

“…To avoid the influences mentioned above, lots of researchers have made contributions on airborne LiDAR point cloud denoising. These denoising algorithms can be classified into three categories, namely algorithms based on morphological operations (Chen et al, 2007;Mongus and Zalik, 2012;Mongus et al, 2014;Li et al, 2013;Li et al, 2014), elevation thresholds setting (Haugerud and Harding, 2001; Silvá n-Cá rdenas and Wang, 2006;Hui et al, 2016) and interpolation fitting (Brovelli et al, 2002;Wang et al, 2009), respectively.…”

Ground filtering and DTM generation from DSM data using probabilistic voting and segmentation

“…Li (2013) proposed a morphological filtering algorithm based on multi-gradient analysis. Hui et al (2016) proposed a multilevel kriging interpolation algorithm with a combination of progressive morphological filtering method.…”