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Automatic Classification of Trees from Laser Scanning Point Clouds : Volume Ii-3/W5, Issue 1 (19/08/2015)

By Sirmacek, B.

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Book Id: WPLBN0004013886
Format Type: PDF Article :
File Size: Pages 8
Reproduction Date: 2015

Title: Automatic Classification of Trees from Laser Scanning Point Clouds : Volume Ii-3/W5, Issue 1 (19/08/2015)  
Author: Sirmacek, B.
Volume: Vol. II-3/W5, Issue 1
Language: English
Subject: Science, Isprs, Annals
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Sirmacek, B., & Lindenbergh, R. (2015). Automatic Classification of Trees from Laser Scanning Point Clouds : Volume Ii-3/W5, Issue 1 (19/08/2015). Retrieved from

Description: Department of Geoscience and Remote Sensing, Delft University of Technology, Stevinweg 1, 2628CN Delft, the Netherlands. Development of laser scanning technologies has promoted tree monitoring studies to a new level, as the laser scanning point clouds enable accurate 3D measurements in a fast and environmental friendly manner. In this paper, we introduce a probability matrix computation based algorithm for automatically classifying laser scanning point clouds into ’tree’ and ’non-tree’ classes. Our method uses the 3D coordinates of the laser scanning points as input and generates a new point cloud which holds a label for each point indicating if it belongs to the ’tree’ or ’non-tree’ class. To do so, a grid surface is assigned to the lowest height level of the point cloud. The grids are filled with probability values which are calculated by checking the point density above the grid. Since the tree trunk locations appear with very high values in the probability matrix, selecting the local maxima of the grid surface help to detect the tree trunks. Further points are assigned to tree trunks if they appear in the close proximity of trunks. Since heavy mathematical computations (such as point cloud organization, detailed shape 3D detection methods, graph network generation) are not required, the proposed algorithm works very fast compared to the existing methods. The tree classification results are found reliable even on point clouds of cities containing many different objects. As the most significant weakness, false detection of light poles, traffic signs and other objects close to trees cannot be prevented. Nevertheless, the experimental results on mobile and airborne laser scanning point clouds indicate the possible usage of the algorithm as an important step for tree growth observation, tree counting and similar applications. While the laser scanning point cloud is giving opportunity to classify even very small trees, accuracy of the results is reduced in the low point density areas further away than the scanning location. These advantages and disadvantages of two laser scanning point cloud sources are discussed in detail.



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