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Article

Point Cloud Classification Algorithm Based on the Fusion of the Local Binary Pattern Features and Structural Features of Voxels

1
Guangxi Key Laboratory of Manufacturing System and Advanced Manufacturing Technology, School of Electrical Engineering, Guangxi University, Nanning 530004, China
2
College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China
*
Author to whom correspondence should be addressed.
Academic Editor: Sander Oude Elberink
Remote Sens. 2021, 13(16), 3156; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163156
Received: 19 July 2021 / Revised: 30 July 2021 / Accepted: 6 August 2021 / Published: 10 August 2021
(This article belongs to the Special Issue Advances in Deep Learning Based 3D Scene Understanding from LiDAR)
Point cloud classification is a key technology for point cloud applications and point cloud feature extraction is a key step towards achieving point cloud classification. Although there are many point cloud feature extraction and classification methods, and the acquisition of colored point cloud data has become easier in recent years, most point cloud processing algorithms do not consider the color information associated with the point cloud or do not make full use of the color information. Therefore, we propose a voxel-based local feature descriptor according to the voxel-based local binary pattern (VLBP) and fuses point cloud RGB information and geometric structure features using a random forest classifier to build a color point cloud classification algorithm. The proposed algorithm voxelizes the point cloud; divides the neighborhood of the center point into cubes (i.e., multiple adjacent sub-voxels); compares the gray information of the voxel center and adjacent sub-voxels; performs voxel global thresholding to convert it into a binary code; and uses a local difference sign–magnitude transform (LDSMT) to decompose the local difference of an entire voxel into two complementary components of sign and magnitude. Then, the VLBP feature of each point is extracted. To obtain more structural information about the point cloud, the proposed method extracts the normal vector of each point and the corresponding fast point feature histogram (FPFH) based on the normal vector. Finally, the geometric mechanism features (normal vector and FPFH) and color features (RGB and VLBP features) of the point cloud are fused, and a random forest classifier is used to classify the color laser point cloud. The experimental results show that the proposed algorithm can achieve effective point cloud classification for point cloud data from different indoor and outdoor scenes, and the proposed VLBP features can improve the accuracy of point cloud classification. View Full-Text
Keywords: point cloud; voxelization; local binary pattern; classification point cloud; voxelization; local binary pattern; classification
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MDPI and ACS Style

Li, Y.; Luo, Y.; Gu, X.; Chen, D.; Gao, F.; Shuang, F. Point Cloud Classification Algorithm Based on the Fusion of the Local Binary Pattern Features and Structural Features of Voxels. Remote Sens. 2021, 13, 3156. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163156

AMA Style

Li Y, Luo Y, Gu X, Chen D, Gao F, Shuang F. Point Cloud Classification Algorithm Based on the Fusion of the Local Binary Pattern Features and Structural Features of Voxels. Remote Sensing. 2021; 13(16):3156. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163156

Chicago/Turabian Style

Li, Yong, Yinzheng Luo, Xia Gu, Dong Chen, Fang Gao, and Feng Shuang. 2021. "Point Cloud Classification Algorithm Based on the Fusion of the Local Binary Pattern Features and Structural Features of Voxels" Remote Sensing 13, no. 16: 3156. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163156

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