The Mixed Kernel Function SVM-Based Point Cloud Classification

Chao Chen, Xiaomin Li, Abdelkader Nasreddine Belkacem, Zhifeng Qiao, Enzeng Dong, Wenjun Tan, Duk Shin

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)737-747
Number of pages11
JournalInternational Journal of Precision Engineering and Manufacturing
Issue number5
Publication statusPublished - May 1 2019


  • Mixed kernel function
  • One-versus-rest (OVR)
  • Point cloud classification
  • Support vector machine (SVM)

ASJC Scopus subject areas

  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering


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