Region-based segmentation on depth images from a 3D reference surface for tree species recognition.

Abstract : The aim of the work presented in this paper is to develop a method for the automatic identification of tree species using Terrestrial Light Detection and Ranging (T-LiDAR) data. The approach that we propose analyses depth images built from 3D point clouds corresponding to a 30 cm segment of the tree trunk in order to extract characteristic shape features used for classifying the different tree species using the Random Forest classifier. We will present the method used to transform the 3D point cloud to a depth image and the region based segmentation method used to segment the depth images before shape features are computed on the segmented images. Our approach has been evaluated using two datasets acquired in two different French forests with different terrain characteristics. The results obtained are very encouraging and promising.
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Contributeur : Alice Ahlem Othmani <>
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  • HAL Id : hal-00923704, version 1

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Ahlem Othmani, Nicolas Lomenie, Piboule Alexandre, Christophe Stolz, Lew Lew Yan Voon. Region-based segmentation on depth images from a 3D reference surface for tree species recognition.. IEEE International Conference on Image Processing, Sep 2013, Melbourne, Australia. pp.0003399, 2013. <hal-00923704>

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