The Mixed Kernel Function SVM-Based Point Cloud Classification
The Mixed Kernel Function SVM-Based Point Cloud Classification
复制标题
基于混合核函数SVM的点云分类
DOI：
10.1007/s12541-019-00102-3
复制
发表时间：
2019-05-01
影响因子：
1.9
通讯作者：
Shin, Duk
中科院分区：
文献类型：
--
作者：
Chen, Chao;Li, Xiaomin;Shin, Duk
利用机载激光雷达测量和探测地面信息是近年来智能传感领域的研究热点之一。提出了一种新的混合核函数支持向量机点云分类算法来区分不同类型的地物。首先提取点云数据的坐标值、RGB值、归一化高程、高程标准差和高程差等组合特征。设计了高斯和多项式混合核函数。然后，一对休息SVM多分类器的构造。最后，利用三维点云数据的特征对SVM分类器进行训练。测试数据的总体分类准确率分别为97.69%和99.13%，两个数据集，I和II。实验结果表明，采用混合核函数的SVM方法的性能优于仅采用高斯核函数和多项式核函数的标准SVM方法，证明了该方法的有效性。
Measurement and detection of ground information by airborne Lidar are one of the hot topics in the field of intelligent sensing in recent years. This study proposes a new point cloud classification algorithm of Mixed Kernel Function SVM to distinguish different types of ground objects. Firstly, the combined features including the coordinate values, the RGB value, normalized elevation, standard deviation of elevation, and elevation difference of point cloud data were extracted. A mixed kernel function of Gauss and Polynomial was designed. Then, one-versus-rest SVM multiple classifiers was constructed. Finally, the feature of 3D point cloud data was employed to train the SVM classifiers. The overall classification accuracies of test data were 97.69% and 99.13% for two data sets, I and II respectively. In addition, the experimental results have showed that the performance of the proposed method with mixed kernel function SVM was better than standard SVM method with Gaussian kernel function and polynomial kernel function only, which demonstrates the effectiveness of the proposed method.