基于积面特征和指向特征的点云植被分类算法  

Point Clouds Classification Algorithm of Vegetation Based on Area and Pointing Features

作　　者：华周阳 徐昇[1] 刘应安[1] Hua Zhouyang;Xu Sheng;Liu Ying’an(College of Information Science and Technology,Nanjing Forestry University,Nanjing 210037,Jiangsu,China)

机构地区：[1]南京林业大学信息科学技术学院,江苏南京210037

出　　处：《激光与光电子学进展》2022年第18期410-416,共7页Laser & Optoelectronics Progress

基　　金：国家重点研发计划(2019YFD1100404);江苏省自然科学基金(青年项目)(BK20200784);中国博士后科学基金面上资助(2019M661852);江苏省高等学校自然科学研究面上项目(19KJB520010)。

摘　　要：为了更好地分析植被的变化,观察林业作物的生长状况,采用地基式激光雷达和手持式激光雷达采集的点云数据,运用机器学习对植被进行分类研究。目前,通过点云协方差矩阵的特征组合进行植被分类存在特征冗余,部分特征的分类效果较差的问题,主要体现在对于植被部位交界处的划分上。为了更加准确地对植被进行分类,研究了基于协方差矩阵的特征提取及Fisher算法的特征选择的点云分类,并提出了积面特征和指向特征,新的特征可以作为支持向量机分类器的输入参数。在地基式激光雷达采集的数据中,两种特征通过Fisher算法计算出的权重分别为7.25和5.78,且积面特征的权重仅次于权重最大的特征λ_(2)(λ_(2)为点云协方差矩阵的特征值),其权重为8.45。使用原特征进行分类的总体精度为99.15%,加入新特征后总体分类精度提高了0.75个百分点,并且对于树干、地面和灌木的交界处的分类效果显著。实验结果表明,所提新特征组合具有较高的权重系数,能够有效提高植被分类精度。对手持式激光雷达采集的数据进行分类的效果也同样较好,使用新特征后总体分类精度达到99.74%,验证了该分类算法具有较强的鲁棒性。In order to better analyze the changes of vegetation and observe the growth status of forestry crops,point cloud data collected by groundbased LiDAR and handheld LiDAR were adopted in this study to conduct classification research on vegetation through machine learning.At present,classification of vegetation based on feature combination of point cloud covariance matrix has redundancy in its features,and the classification effect of some features is poor.It is mainly reflected in the classification of the boundary of vegetation.To classify vegetation more accurately,this study investigated the point cloud classification based on covariance matrix feature extraction and Fisher algorithm feature selection,and proposed two features of input parameters of support vector machine(SVM)classifier,namely,area feature and pointing feature.In the data collected by the groundbased LIDAR,the weights of the two features that were calculated by Fisher algorithm were 7.25 and 5.78,respectively.The weight of the area feature ranked second only compared to the featureλ_(2) with the highest weight of 8.45(λ_(2) is the eigenvalue of the point cloud covariance matrix).The overall classification accuracy using the original features is 99.15%;the overall classification accuracy was improved by 0.75 percentage points after the addition of the new features.Moreover,the classification effect of the junction of tree trunk,ground,and shrub was remarkable.The results showed that the proposed new feature combination has a higher weight coefficient,which can effectively improve the accuracy of vegetation classification.The classification effect of the data collected by the handheld LiDAR was satisfactory.The overall classification accuracy reaches 99.74%after using the new feature,which verified the strong robustness of the classification algorithm.

关 键 词：遥感 激光雷达 机器学习 特征选择 植被分类 支持向量机 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]