0000000000584988
AUTHOR
Ahlem Othmani
Towards automated and operational forest inventories with T-Lidar
International audience; Forest inventory automation has become a major issue in forestry. The complexity of the segmentation of 3D point cloud is due to mutual occlusion between trees, other vegetation, or branches. That is why, the applications done until now are limited to the estimation of the DBH (Diameter at Breast Height), the tree height and density estimation. Furthermore other parameters could also be detected, such as volume or species of trees (Reulke and Haala) . . . This paper presents an effective approach for automatic detection, isolation of trees and DBH estimation. Tree isolation is achieved using an innovative approach based on a clustering methodology followed by a skele…
Region-based segmentation on depth images from a 3D reference surface for tree species recognition.
International audience; 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 be…
Ontology-driven Image Analysis for Histopathological Images
International audience; Ontology-based software and image processing engine must cooperate in new fields of computer vision like microscopy acquisition wherein the amount of data, concepts and processing to be handled must be properly controlled. Within our own platform, we need to extract biological objects of interest in huge size and high-content microscopy images. In addition to specific low-level image analysis procedures, we used knowledge formalization tools and high-level reasoning ability of ontology-based software. This methodology made it possible to improve the expressiveness of the clinical models, the usability of the platform for the pathologist and the sensitivity or sensibi…
Automatic recognition of tree species from 3D point clouds of forest plots
The objective of the thesis is the automatic recognition of tree species from Terrestrial LiDAR data. This information is essential for forest inventory. As an answer, we propose different recognition methods based on the 3D geometric texture of the bark.These methods use the following processing steps: a preprocessing step, a segmentation step, a feature extraction step and a final classification step. They are based on the 3D data or on depth images built from 3D point clouds of tree trunks using a reference surface.We have investigated and tested several segmentation approaches on depth images representing the geometric texture of the bark. These approaches have the disadvantages of over…
Identification des espèces d'arbres à partir de données T-LiDAR Tree species identification using T-LiDAR data
National audience; En raison de l'utilisation croissante des scanners LiDAR terrestre (T-LiDAR) dans le domaine forestier, le développement d'outils logiciels pour la mesure automatique d'attributs d'inventaire forestier est devenu un domaine de recherche important. De nombreux travaux portant sur la localisation des arbres dans un nuage de points, la mesure du diamètre à hauteur de poitrine (DHP) ou la mesure de la hauteur des arbres ont été décrits dans la littérature. Cependant, le problème de l'identification des espèces d'arbres à partir de données T-LiDAR a été peu abordé. La plupart des travaux utilisent des données LiDAR aéroportées et les espèces des arbres sont déterminées à l'éch…