Full-Waveform LiDAR Point Clouds Classification Based on Wavelet Support Vector Machine and Ensemble Learning

• Published in: Sensors (Basel, Switzerland) Switzerland), 2019-07, Vol.19 (14), p.3191
• Main Authors: Lai, Xudong, Yuan, Yifei, Li, Yongxu, Wang, Mingwei
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Backscattering; Classification; ensemble learning; Fault diagnosis; full-waveform LiDAR; International conferences; Laboratories; Localization; Methods; Neural networks; Optimization; point cloud classification; R&D; Remote sensing; Research & development; support vector machine; Support vector machines; Waveforms; Wavelet analysis; wavelet kernel function
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19143191

Light Detection and Ranging (LiDAR) produces 3D point clouds that describe ground objects, and has been used to make object interpretation in many cases. However, traditional LiDAR only records discrete echo signals and provides limited feature parameters of point clouds, while full-waveform LiDAR (FWL) records the backscattered echo in the form of a waveform, which provides more echo information. With the development of machine learning, support vector machine (SVM) is one of the commonly used classifiers to deal with high dimensional data via small amount of samples. Ensemble learning, which combines a set of base classifiers to determine the output result, is presented and SVM ensemble is used to improve the discrimination ability, owing to small differences in features between different types of data. In addition, previous kernel functions of SVM usually cause under-fitting or over-fitting that decreases the generalization performance. Hence, a series of kernel functions based on wavelet analysis are used to construct different wavelet SVMs (WSVMs) that improve the heterogeneity of ensemble system. Meanwhile, the parameters of SVM have a significant influence on the classification result. Therefore, in this paper, FWL point clouds are classified by WSVM ensemble and particle swarm optimization is used to find the optimal parameters of WSVM. Experimental results illustrate that the proposed method is robust and effective, and it is applicable to some practical work.