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Vol. 156, Issue 9, September 2013, pp. 217-226

 

Bullet

 

Computational-geometry-based Plant Organs Classification and Foliage 3D Reconstruction from Point Cloud Data
 
Ting Yun, Mingxing Gao, Yanming Wang, Xiaofeng Liu

Nanjing Forestry University, Longpan Road, No.159, Nanjing, 210037, China
Tel.: 13585100839, fax: 025-85427687
E-mail: njyunting@qq.com, liuxiaofeng.njfu@hotmail.com

 

Received: 5 June 2013   /Accepted: 25 August 2013   /Published: 30 September 2013

Digital Sensors and Sensor Sysstems

 

Abstract: In recent years Terrestrial Laser Scaner (TLS) is widely used in complex scene survey and space objects measurement, however, due to the trees' irregular and complex morphology, also the scanning results be effected by the wind blowing and occlusion effect, so quantifying the 3-D morphology structure and forestry index of an individual tree or a forest stand from Point Cloud Data (PCD) is a challenging task. In this paper, the computer theory is combined into our approach. Firstly the covariance matrixes based on neighborhood information are constructed to retrieve the feature vectors of every PCD, including normal vector, torsion and curvature from the scanning data. Secondly the LLE manifold-learning method is adopted for dimensionality reduction of PCD features, then identification and classification of different plant organs are achieved. Finally orthogonal least squares algorithm about three-dimensional surface fitting is presented to remove deviation caused by leaf jitter, then the whole PCD of singe leave are mapped onto one three-dimensional surface, next, many triangles are used to form the foliage area and Leaf Area Index (LAI) can be calculated based on the delaunay triangulation algorithm. In this paper we apply computer theory to overcome the shortcomings of TLS in forestry application, automatically and nondestructively achieve the classification of different plant organs and 3-D reconstruction of real foliage, most importantly, this work provide a theoretical foundation for retrieving LAI and forestry parameters from PCD obtained with a TLS.

 

Keywords: Terrestrial laser scanning (TLS), Point cloud data (PCD), Plant organs classification, Foliage 3-D reconstruction.

 

 

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