Tree Species Classification Using Optimized Features Derived from Light Detection and Ranging Point Clouds Based on Fractal Geometry and Quantitative Structure Model
Tree Species Classification Using Optimized Features Derived from Light Detection and Ranging Point Clouds Based on Fractal Geometry and Quantitative Structure Model
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DOI：
10.3390/f14061265
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发表时间：
2023-06
期刊：
影响因子：
2.9
通讯作者：
Z. Hui;Zhaochen Cai;Peng Xu;Yuanping Xia;P. Cheng
中科院分区：
文献类型：
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作者：
Z. Hui;Zhaochen Cai;Peng Xu;Yuanping Xia;P. Cheng
树种分类是森林清查领域普遍存在的任务。大多数现有的使用激光雷达技术进行树种分类的方法仅应用直接测量的特征向量。结果，很难获得令人满意的树种分类性能。为了解决这一挑战，本文的作者开发了两种新的特征向量，包括基于分形几何的特征向量和基于定量结构模型（QSM）的特征向量。在分形几何方面，提取两个分形参数作为特征向量，以反映树结构在三维空间中的分布情况。在QSM方面，提取不同分支的长度变化比和半径变化比作为特征向量。为了降低特征向量维数并挖掘有价值的特征向量，使用分类回归树（CART）进行特征向量降维。选择了五种树种的五百六十八棵单树来评估所开发的特征向量的性能。实验结果表明，树种水青冈的总体准确率最高，为98.06%，而栎树的总体准确率最低，为96.65%。采用其他四种经典的监督学习方法进行比较。比较结果表明，无论采用哪种精度指标，所提出的方法都优于其他四种监督学习方法。与相关方法相比，本文开发的八个特征向量也表现得更好。这表明本文开发的基于分形几何的特征向量和基于QSM的特征向量可以有效提高树种分类的性能。
Tree species classification is a ubiquitous task in the forest inventory field. Only directly measured feature vectors have been applied to most existing methods that use LiDAR technology for tree species classification. As a result, it is difficult to obtain a satisfactory tree species classification performance. To solve this challenge, the authors of this paper developed two new kinds of feature vectors, including fractal geometry-based feature vectors and quantitative structural model (QSM)-based feature vectors. In terms of fractal geometry, both two fractal parameters were extracted as feature vectors for reflecting how tree architecture is distributed in three-dimensional space. In terms of QSM, the ratio of length change and the ratio of radius change of different branches were extracted as feature vectors. To reduce the feature vector dimensionality and explore valuable feature vectors, feature vector dimension reduction was conducted using the classification and regression tree (CART). Five hundred and sixty-eight individual trees with five tree species were selected for evaluating the performance of the developed feature vectors. The experimental results indicate that the tree species of Fagus sylvatica achieved the highest overall accuracy, which is 98.06%, while Quercus petraea obtained the lowest overall accuracy, which is 96.65%. Four other classical supervised learning methods were adopted for comparison. The comparison result indicates that the proposed method outperformed the other four supervised learning methods no matter which accuracy indicator was adopted. In comparison with the relevant method, the eight feature vectors developed in this paper also performed much better. This indicates that the fractal geometry-based feature vectors and QSM-based feature vectors developed in this paper can effectively improve the performance of tree species classification.