Non-parametric segmentation of ALS point clouds using mean shift

By T. Melzer. 2007

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Assessment of Low Density Full-Waveform Airborne Laser Scanning for Individual Tree Detection and Tree Species Classification (2014, background): “In addition, FWF-derived features have been demonstrated to be useful in tree species classification [29–31], for segmenting ALS point clouds in city areas [32] and in estimation of 3D vegetation structure [28,33].”
Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests (2011, methodology): “Several classification methods were applied to lidar data for urban scenes such as the unsupervised Mean-Shift algorithm (Melzer, 2007), supervised classification such as Support Vector Machines (SVM) (Secord and Zakhor, 2007; Mallet et al.”
An update on automatic 3D building reconstruction (2010, unknown): “An in-depth description on how this nonparametric segmentation can be applied to 3D point clouds is e.g. given in Melzer (2007).”