3D segmentation of single trees exploiting full waveform LIDAR data

By J. Reitberger, Claudius Schnörr, Peter Krzystek, Uwe Stilla. 2009

489 citations recorded by the source corpus.

Subfields: Environmental Engineering, Insect Science, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/1c7482168f4a0baf18f66049849dd9f2ce0e1e57

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In Environmental Engineering

In Insect Science

In Nature and Landscape Conservation

Citations in the corpus

Three-dimensional surface reconstruction of tree canopy from lidar point clouds using a region-based level set method (2013, methodology): “salient features) for segmentation and tree species classification when the previous models are applied to obtain the tree height, crown width, basal area, crown base height, and crown volume using lidar data (Brandtberg et al. 2003; Koch, Heyder, and Weinacker 2006; Lee and Lucas 2007; Reitberger e”
Comparison of airborne laser scanning methods for estimating forest structure indicators based on Lorenz curves (2014, background): “…be obtained with improved tree detection algorithms (Lähivaara et al., 2013), direct point cloud segmentation (Li et al., 2012), using full-waveform ALS information (Reitberger et al., 2009), or analyzing combined leaf-on and leaf-off acquisitions in deciduous forests (Hill and Broughton, 2009).”
Contextual classification of lidar data and building object detection in urban areas (2014, methodology): “Airborne LiDAR (Light Detection And Ranging) is a particularly useful technology for the acquisition of elevation data, with applications such as the generation of digital terrain models (DTM) (Kraus and Pfeifer, 1998), data acquisition for forestry (Reitberger et al., 2009), or power line monitorin”
Analysis of full-waveform LiDAR data for classification of an orange orchard scene (2013, unknown): “Reitberger et al. (2009) enhanced the watershed segmentation procedure by adding a special stem detection method and three-dimensional segmentation of single trees using a normalized cut method.”
Trends in Automatic Individual Tree Crown Detection and Delineation - Evolution of LiDAR Data (2016, methodology): “[81] combined conventional watershed segmentation with a stem detection method that detected stem positions by hierarchically clustering points below the crown base height, reconstructing the stems with a robust RANSAC-based estimation, and segmenting 3D single trees with normalized cut segmentation”
A Benchmark of Lidar-Based Single Tree Detection Methods Using Heterogeneous Forest Data from the Alpine Space (2015, background): “[49] conclude that the laser point density has less impact on the individual tree detection.”
Assessment of Low Density Full-Waveform Airborne Laser Scanning for Individual Tree Detection and Tree Species Classification (2014, methodology): “However, the obtained detection rates are comparable to the results obtained with denser ALS data [56,57] and thus the use of low-density FWF could be beneficial for forest industry to get more detailed size information of the standing trees if detection rate can be improved as for example in [35] w”
Potential of UltraCamX stereo images for estimating timber volume and basal area at the plot level in mixed European forests (2013, background): “2008), for individual tree crown delineation (e.g., Persson et al. 2002; Koch et al. 2006; Reitberger et al. 2009), for tree species classification (Heinzel and Koch 2011), and even for modeling complex forest canopy (Morsdorf et al.”
Tree species classification and estimation of stem volume and DBH based on single tree extraction by exploiting airborne full-waveform LiDAR data (2012, methodology): “The key idea of the new 3D segmentation technique (Reitberger et al., 2009) is to rigorously apply normalized cut segmentation (Shi & Malik, 2000) to a voxel representation of the forest area.”
Comparative testing of single-tree detection algorithms under different types of forest (2011, background): “A similar conclusion was reported by Reitberger et al. (2009), who highlighted the benefit of full waveform data especially in segmentation task.”
Analysis of full-waveform LiDAR data for forestry applications: a review of investigations and methods (2011, methodology): “* Reitberger et al. (2009b) Use Normalized cut segmentation and unsupervised classification based on waveform metrics to detect single trees also in lower layer.”