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Article dans une revue

A novel Computer-Aided Tree Species Identification method based on Burst Wind Segmentation of 3D bark textures

Abstract : Terrestrial Laser Scanning (TLS) systems have gained increasing popularity in the forestry domain and are today widely used for the automatic measurement of forest inventory attributes. Nevertheless, to the best of our knowledge the problem of tree species recognition from TLS data has received very little attention from the scientific community. It is in this context that we present a novel Computer-Aided Tree Species Identification method based on 3D bark texture analysis. The novelty of our approach resides in the following three key points: (1) 3D salient regions extraction using a new morphological segmentation method that we have called Burst Wind Segmentation, (2) the extraction and pre-annotation of a collection of typical 3D bark patterns, known as scars, from each of the tree species. The pre-annotated scars are stored in a dictionary that we have called ScarBook and they are used as a reference for the comparison of the 3D salient segmented regions, (3) a wide variety of advanced shape, saliency, curvature and roughness features are extracted from the 3D salient segmented regions. To study the performance of our method, an experiment has been carried out on a dataset composed of 969 patches which correspond to 30 cm long segments of the trunk at breast height. Six species among the most dominant species in European forests have been tested with patches of different diameter at breast height values so as to study the identification accuracy with respect to age. The results obtained are very encouraging and promising and they confirm the possibility of identifying tree species using TLS data.
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Contributeur : Le2i - Université de Bourgogne <>
Soumis le : vendredi 13 janvier 2017 - 12:34:18
Dernière modification le : vendredi 21 mai 2021 - 03:44:18



Alice Ahlem Othmani, Cansen Jiang, Nicolas Lomenie, Jean-Marie Favreau, Alexandre Piboule, et al.. A novel Computer-Aided Tree Species Identification method based on Burst Wind Segmentation of 3D bark textures. Machine Vision and Applications, Springer Verlag, 2016, 27 (5), pp.751 - 766. ⟨10.1007/s00138-015-0738-2⟩. ⟨hal-01434595⟩



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