Single tree species classification from Terrestrial Laser Scanning data for forest inventory

Autor: Lew F.C. Lew Yan Voon, Christophe Stolz, Alexandre Piboule, Ahlem Othmani
Přispěvatelé: 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 ), ONF R&D department ( ONF )
Rok vydání: 2013
Předmět:
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
Zdroj: Pattern Recognition Letters
Pattern Recognition Letters, Elsevier, 2013, 34 (16), pp.2144-2150. ⟨10.1016/j.patrec.2013.08.004⟩
Pattern Recognition Letters, Elsevier, 2013, 34 (16), pp.2144-2150. 〈10.1016/j.patrec.2013.08.004〉
ISSN: 0167-8655
DOI: 10.1016/j.patrec.2013.08.004
Popis: 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.
Databáze: OpenAIRE
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