SLOPE BASED FILTERING OF LASER ALTIMETRY DATA

By George Vosselman. 2000

802 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0f60d1171a79fd969a3e7b1c5eb5d2386c134f02

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Testing LiDAR models of fractional cover across multiple forest ecozones (2009, methodology): “All datasets were classified to extract ground returns (RGround) using the Terrascan morphological ground classification filter (e.g. Axelsson, 1999; Vosselman, 2000) to provide a digital elevation model (DEM) to which all laser pulse return heights could be normalised.”
FILTERING OF LASER ALTIMETRY DATA USING A SLOPE ADAPTIVE FILTER (2001, methodology): “However, this assumption limits the use of the filter to terrain with gentle slopes.”
An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation (2016, methodology): “Vosselman [2] developed a slope-based filtering algorithm by comparing slopes between a LiDAR point and its neighbors.”
Whitebox GAT: A case study in geomorphometric analysis (2016, methodology): “Whitebox GAT's Isolate Ground Points tool, which performs a slope-based filter similar to that of Vosselman (2000), was used for this purpose.”
Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds (2004, methodology): “Marco Roggero This filter is a variant on the morphological filter developed by Vosselman (Vosselman, 2000; Vosselman and Maas, 2001).”
A progressive morphological filter for removing nonground measurements from airborne LIDAR data (2003, methodology): “Another commonly used algorithm to remove nonground objects is a mathematical morphology filter which is applied to a grayscale image [4], [10], [11].”
Airborne LiDAR for DEM generation: some critical issues (2008, background): “The definition of a reasonable threshold should incorporate the knowledge about terrain in the study area (Vosselman, 2000; Zhang et al., 2003).”
Eigen-feature analysis of weighted covariance matrices for LiDAR point cloud classification (2014, methodology): “In the classification of LiDAR data, various techniques were proposed, including unsupervised classification (Haala and Brenner, 1999; Vosselman, 2000) and supervised classification, such as Bayesian networks (Stassopoulou et al., 2000), decision trees (Antonarakis et al., 2008), and support vector…”