Lai Xudong, Yuan Yifei, Li Yongxu, Wang Mingwei
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.
Key Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and Geoinformation, Wuhan 430079, China.
Sensors (Basel). 2019 Jul 19;19(14):3191. 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.
激光探测与测距（LiDAR）生成描述地面物体的三维点云，并已在许多情况下用于物体解释。然而，传统LiDAR仅记录离散回波信号并提供有限的点云特征参数，而全波形LiDAR（FWL）以波形形式记录后向散射回波，提供了更多的回波信息。随着机器学习的发展，支持向量机（SVM）是通过少量样本处理高维数据的常用分类器之一。提出了一种将一组基分类器组合起来确定输出结果的集成学习方法，并使用SVM集成来提高判别能力，这是由于不同类型数据之间的特征差异较小。此外，SVM以前的核函数通常会导致欠拟合或过拟合，从而降低泛化性能。因此，使用一系列基于小波分析的核函数来构建不同的小波支持向量机（WSVM），以提高集成系统的异质性。同时，SVM的参数对分类结果有显著影响。因此，本文采用WSVM集成对FWL点云进行分类，并使用粒子群优化算法寻找WSVM的最优参数。实验结果表明，该方法具有鲁棒性和有效性，适用于一些实际工作。
