Hybrid segmentation of depth images using a watershed and region merging based method for tree species recognition

Abstract : Tree species recognition from Terrestrial Light Detection and Ranging (T-LiDAR) scanner data is essential for estimating forest inventory attributes in a mixed planting. In this paper, we propose a new method for individual tree species recognition based on the analysis of the 3D geometric texture of tree barks. Our method transforms the 3D point cloud of a 30 cm segment of the tree trunk into a depth image on which a hybrid segmentation method using watershed and region merging techniques is applied in order to reveal bark shape characteristics. Finally, shape and intensity features are calculated on the segmented depth image and used to classify five different tree species using a Random Forest (RF) classifier. Our method has been tested using two datasets acquired in two different French forests with different terrain characteristics. The accuracy and precision rates obtained for both datasets are over 89%.
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Communication dans un congrès
11 th IEEE IVMSP workshop : 3D Image / Video Technologies and Applications ( IVMSP 2013), Jun 2013, Seoul, South Korea. pp.1 - 4, 2013, <10.1109/IVMSPW.2013.6611901>
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https://hal.archives-ouvertes.fr/hal-01116700
Contributeur : Alice Ahlem Othmani <>
Soumis le : samedi 14 février 2015 - 10:43:38
Dernière modification le : jeudi 23 avril 2015 - 13:49:18

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Ahlem Othmani, Alexandre Piboule, Yan Voon. Hybrid segmentation of depth images using a watershed and region merging based method for tree species recognition. 11 th IEEE IVMSP workshop : 3D Image / Video Technologies and Applications ( IVMSP 2013), Jun 2013, Seoul, South Korea. pp.1 - 4, 2013, <10.1109/IVMSPW.2013.6611901>. <hal-01116700>

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