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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Extracting Wood Point Cloud of Individual Trees Based on Geometric Features
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Extracting Wood Point Cloud of Individual Trees Based on Geometric Features

机译：基于几何特征提取单木的木点云

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摘要

The wood structure is an important parameter that represents the geometrical and topological characteristics of trees. Accurate extraction of the wood component of a tree is of great importance in visualizing trees. Light detection and ranging (LiDAR) has been applied to obtain the 3-D structural properties of vegetation. However, it is difficult to separate the wood and leaf components from point clouds data in situations where wood and leaves are mixed and overlapping. This letter proposes an effective method for extracting wood point cloud of individual trees based on different geometric features of leaves and wood by combining classification and segmentation methods. Magnolia grandiflora and Cinnamoinuin camphor trees were scanned using a high-resolution terrestrial LiDAR. The complexity of the structure of the canopy was reduced using a slicing method. The wood and/or leaf components were classified using the K-means and random sampling consistency (RANSAC) algorithm. The cylindrical segmentation method based on the RANSAC algorithm was used for the precise extraction of the wood component in wood and leaf mixed point clouds. The trunk under the canopy was extracted completely. The average recall and precision in extracting canopy wood point clouds of the Magnolia grandiflora and Cinnamomum camphor achieved 94.60%, 92.02% and 93.62%, 91.46%, respectively. The results indicate that the proposed method has the potential for accurately extracting the wood point cloud from terrestrial LiDAR data.
机译：木材结构是代表树木的几何和拓扑特征的重要参数。准确提取树木的木材成分对于可视化树木非常重要。光检测和测距（LiDAR）已应用于获取植被的3-D结构特性。但是，在木头和树叶混合和重叠的情况下，很难从点云数据中分离出木头和树叶的成分。这封信提出了一种有效的方法，该方法通过结合分类和分割方法，根据叶子和木材的不同几何特征提取单个树木的木材点云。使用高分辨率陆地激光雷达对木兰和肉桂樟树进行了扫描。使用切片方法降低了顶篷结构的复杂性。使用K均值和随机抽样一致性（RANSAC）算法对木材和/或叶子的成分进行分类。基于RANSAC算法的圆柱分割方法用于木材和叶片混合点云中木材成分的精确提取。树冠下的树干被完全抽出。木兰和肉桂樟的冠层木点云提取的平均召回率和精确度分别达到94.60％，92.02％和93.62％，91.46％。结果表明，该方法具有从地面LiDAR数据中准确提取木点云的潜力。

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