Segmentation of tree regions using data of a full-waveform laser

By H. Gross, B. Jutzi, Ulrich Thoennessen. 2007

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DIMENSIONALITY BASED SCALE SELECTION IN 3D LIDAR POINT CLOUDS (2012, methodology): “As enhanced in Figure 1 and for instance in (Gross et al., 2007), such an ellipsoid reveals the linear, planar or volumetric behaviour of the neighborhood i.e., whether the point set is spread in one, two or three dimensions (blue, gray, and green ellipsoids in Figure 1, respectively).”
Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests (2011, methodology): “In Gross et al. (2007) and Wagner et al. (2008), geometric and FW lidar features are derived from a FW 3D point cloud and used jointly to discriminate vegetated areas.”
Eigen-feature analysis of weighted covariance matrices for LiDAR point cloud classification (2014, methodology): “Some features are defined based on waveform considerations (Gross et al., 2007; Wagner et al., 2008; Rutzinger et al., 2008), and some features are related to the geometric information of point clouds, such as surface slopes, point heights, and local geometric shapes (Charaniya et al.”
Full-waveform topographic lidar : State-of-the-art (2009, background): “The features currently extracted from a modeling step often have values similar to natural objects (Gross et al., 2007; Mallet et al., 2008).”