1. Single tree species classification from Terrestrial Laser Scanning data for forest inventory
- Author
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Lew F.C. Lew Yan Voon, Christophe Stolz, Alexandre Piboule, Ahlem Othmani, Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i), Université de Bourgogne (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), ONF R&D department (ONF), ONF, Laboratoire Electronique, Informatique et Image ( Le2i ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS ), and ONF R&D department ( ONF )
- Subjects
010504 meteorology & atmospheric sciences ,Laser scanning ,Computer science ,02 engineering and technology ,computer.software_genre ,01 natural sciences ,Artificial Intelligence ,0202 electrical engineering, electronic engineering, information engineering ,ComputingMilieux_MISCELLANEOUS ,Single tree species classification Forest inventory 3D point cloud flattening 3D geometric texture classification ,0105 earth and related environmental sciences ,Forest inventory ,business.industry ,Diameter at breast height ,Wavelet transform ,Pattern recognition ,15. Life on land ,Contourlet ,Random forest ,visual_art ,Signal Processing ,[SPI.OPTI]Engineering Sciences [physics]/Optics / Photonic ,visual_art.visual_art_medium ,020201 artificial intelligence & image processing ,Bark ,[ SPI.OPTI ] Engineering Sciences [physics]/Optics / Photonic ,Computer Vision and Pattern Recognition ,Artificial intelligence ,Data mining ,business ,computer ,Classifier (UML) ,Software - Abstract
Due to the increasing use of Terrestrial Laser Scanning (TLS) systems in the forestry domain for forest inventory, the development of software tools for the automatic measurement of forest inventory attributes from TLS data has become a major research field. Numerous research work on the measurement of attributes such as the localization of the trees, the Diameter at Breast Height (DBH), the height of the trees, and the volume of wood has been reported in the literature. However, to the best of our knowledge the problem of tree species recognition from TLS data has received very little attention from the scientific community. Most of the research work uses Airborne Laser Scanning (ALS) data and measures tree species attributes on large scales. In this paper we propose a method for individual tree species classification of five different species based on the analysis of the 3D geometric texture of the bark. The texture features are computed using a combination of the Complex Wavelet Transforms (CWT) and the Contourlet Transform (CT), and classification is done using the Random Forest (RF) classifier. The method has been tested using a dataset composed of 230 samples. The results obtained are very encouraging and promising.
- Published
- 2013
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