The Use of 3D Descriptors and Intensity to Detect Trees from LiDAR Data in Urban Environment
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Abstract
The result derived from tree detection using LiDAR data can be used in different applications such as forest management and preservation, urban planning, detection of occluded objects by tree crowns, among others. In this sense, this work aims to evaluate the applicability of 3D geometric descriptors based on eigenvalues and LiDAR intensity in tree detection. In experiments, it was analyzed the influence of neighborhood (sphere and cylinder) in the calculation of geometric attributes. From visual analyses, it was possible to notice that the use of some geometric attributes such as omnivariance, curvature, planarity and eigenentropy showed more potential in detecting trees. Considering the four selected attributes (omnivariance, curvature, planarity, eigenentropy) and intensity, the K-Means algorithm was executed to each attribute aiming to separate the point cloud into tree and non-tree. The results derived from classification were compared with reference data using quality parameters such as completeness, correctness and F-Score. Completeness values obtained with for omnivariance, eigenentropy, planarity and intensity reached values above 90%, indicating that these descriptors have high reliability for tree detection. The average correctness was around 62%, presenting a large number of false positives. When analyzing F-Score values, it was possible to verify the potential of omnivariance, computed in a spherical neighborhood, in detecting trees from LiDAR data (F-Score above 80%).
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