Deep learning-based tree classification using mobile LiDAR data
Deep learning-based tree classification using mobile LiDAR data
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DOI：
10.1080/2150704x.2015.1088668
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发表时间：
2015-11-02
影响因子：
2.3
通讯作者：
Zhang, Qi
中科院分区：
文献类型：
--
作者：
Guan, Haiyan;Yu, Yongtao;Zhang, Qi
我们的工作解决了从移动激光雷达数据中提取和分类树种的问题。本文的工作包括树的预处理和树的分类。在树的预处理中，提出了基于体素的向上生长滤波来去除移动LiDAR数据中的地面点，然后通过欧几里德距离聚类和基于体素的归一化切割分割来提取单独的树。在树木分类中，首先提出一种波形表示法来模拟树木的几何结构。然后，使用深度学习技术来生成树的波形表示的高级特征抽象。定量分析表明，该算法在利用移动激光雷达数据进行城市树种分类时，总体准确率为86.1%，kappa系数为0.8。对比实验表明，波形表示和深度Boltzmann机器的使用有助于提高树种的分类精度。
Our work addresses the problem of extracting and classifying tree species from mobile LiDAR data. The work includes tree preprocessing and tree classification. In tree preprocessing, voxel-based upward-growing filtering is proposed to remove ground points from the mobile LiDAR data, followed by a tree segmentation that extracts individual trees via Euclidean distance clustering and voxel-based normalized cut segmentation. In tree classification, first, a waveform representation is developed to model geometric structures of trees. Then, deep learning techniques are used to generate high-level feature abstractions of the trees' waveform representations. Quantitative analysis shows that our algorithm achieves an overall accuracy of 86.1% and a kappa coefficient of 0.8 in classifying urban tree species using mobile LiDAR data. Comparative experiments demonstrate that the uses of waveform representation and deep Boltzmann machines contribute to the improvement of classification accuracies of tree species.