Individual tree detection in Light Detection and Ranging (LiDAR) data has been widely investigated in the literature. However, most of the methods work well on conifers but lead to poor accuracy in broad-leaved forest. The detection of deciduous trees is a complex task due to: (i) multiple local maxima present in the same canopy, and (ii) the tree-top (TP) can be in a different location from the canopy center. This paper presents an automatic technique which exploits high density LiDAR data to refine the detection of deciduous trees. First, the candidate tree-tops (CTPs) are detected using the standard level set method (LSM). Then, the Delaunay triangulation is used to generate a network topology which connects neighboring CTPs. For each pair of connected CTPs, geometrical features are extracted to automatically determine if the CTPs pair belongs to the same tree or to different canopies. The groups of CTPs identified as belonging to the same tree crown are merged into one TP. Preliminary numerical results show that the proposed method halves the commission errors of the initial TP detection by increasing the overall detection accuracy of 8.2%.

An Automatic Technique for Deciduous Trees Detection in High Density Lidar Data Based on Delaunay Triangulation / Marinelli, Daniele; Paris, Claudia; Bruzzone, Lorenzo. - (2019), pp. 94-97. ((Intervento presentato al convegno IGARSS 2019 tenutosi a Yokohama nel 28th July-2nd August 2019 [10.1109/IGARSS.2019.8899772].

An Automatic Technique for Deciduous Trees Detection in High Density Lidar Data Based on Delaunay Triangulation

Marinelli, Daniele;Paris, Claudia;Bruzzone, Lorenzo
2019

Abstract

Individual tree detection in Light Detection and Ranging (LiDAR) data has been widely investigated in the literature. However, most of the methods work well on conifers but lead to poor accuracy in broad-leaved forest. The detection of deciduous trees is a complex task due to: (i) multiple local maxima present in the same canopy, and (ii) the tree-top (TP) can be in a different location from the canopy center. This paper presents an automatic technique which exploits high density LiDAR data to refine the detection of deciduous trees. First, the candidate tree-tops (CTPs) are detected using the standard level set method (LSM). Then, the Delaunay triangulation is used to generate a network topology which connects neighboring CTPs. For each pair of connected CTPs, geometrical features are extracted to automatically determine if the CTPs pair belongs to the same tree or to different canopies. The groups of CTPs identified as belonging to the same tree crown are merged into one TP. Preliminary numerical results show that the proposed method halves the commission errors of the initial TP detection by increasing the overall detection accuracy of 8.2%.
2019 IEEE International Geoscience and Remote Sensing Symposium Proceedings
Piscataway, USA
IEEE
978-1-5386-9154-0
Marinelli, Daniele; Paris, Claudia; Bruzzone, Lorenzo
An Automatic Technique for Deciduous Trees Detection in High Density Lidar Data Based on Delaunay Triangulation / Marinelli, Daniele; Paris, Claudia; Bruzzone, Lorenzo. - (2019), pp. 94-97. ((Intervento presentato al convegno IGARSS 2019 tenutosi a Yokohama nel 28th July-2nd August 2019 [10.1109/IGARSS.2019.8899772].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/249685
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