Abstract: A method for extracting underground target based on local entropy of discrete point clouds after voxelization of 3D ground penetrating radar (3D GPR) data is proposed to address the problem of insufficient exploration of 3D spatial information by 3D ground penetrating radar and the data processing mainly based on analysis and interpretation of 2D slice images. Firstly, the obtained 3D ground penetrating radar data was voxelized into discrete 3D point clouds. Then, the local entropy of the voxelized point cloud for the entire region were calculated. The soil background and underground targets were distinguished by classifying them from multiple dimensions through support vector machine (SVM). Finally, taking the underground environment of urban roads as the research object, it is used to conduct experimental analysis using measured data. The experimental results show that the accuracy of this method in extracting underground targets is as high as 84.2%, and the missed detection rate of underground targets is as low as 9.8%. This method is accurate and effective, providing a new approach for 3D ground penetrating radar to extract underground target.

Key words: 3D ground penetrating radar, underground target extraction, local entropy, voxelization