论文标题
基于树木陆点云的木叶分类的自动采样和训练方法
Automatic sampling and training method for wood-leaf classification based on tree terrestrial point cloud
论文作者
论文摘要
陆地激光扫描技术为获取植物的三维信息提供了有效而准确的解决方案。植物点云数据的叶木分类是某些林业和生物学研究的基本步骤。根据树点云数据提出了一种自动采样和分类训练方法。平面拟合方法用于自动选择叶子样品点和木材样品点，然后使用支持向量机（SVM）算法计算两个局部特征，以进行训练和分类。使用建议的方法和手动选择方法测试了十棵树的点云数据。平均正确分类率和KAPPA系数分别为0.9305和0.7904。结果表明，与手动选择方法相比，所提出的方法具有更好的效率和准确性。
Terrestrial laser scanning technology provides an efficient and accuracy solution for acquiring three-dimensional information of plants. The leaf-wood classification of plant point cloud data is a fundamental step for some forestry and biological research. An automatic sampling and training method for classification was proposed based on tree point cloud data. The plane fitting method was used for selecting leaf sample points and wood sample points automatically, then two local features were calculated for training and classification by using support vector machine (SVM) algorithm. The point cloud data of ten trees were tested by using the proposed method and a manual selection method. The average correct classification rate and kappa coefficient are 0.9305 and 0.7904, respectively. The results show that the proposed method had better efficiency and accuracy comparing to the manual selection method.
