| 机载多光谱LiDAR数据的地物分类方法 |
| |
| 引用本文： | 潘锁艳,管海燕.机载多光谱LiDAR数据的地物分类方法[J].测绘学报,2018,47(2):198-207. |
| |
| 作者姓名： | 潘锁艳 管海燕 |
| |
| 作者单位： | 南京信息工程大学遥感与测绘工程学院, 江苏 南京 210044 |
| |
| 基金项目： | 国家自然科学基金(41671454)The National Natural Science Foundation of China (41671454) |
| |
| 摘 要： | 机载多光谱LiDAR系统能够快速地获取大范围地表面上地物光谱和几何数据，并能够保证所获取的光谱与空间几何数据在空间和时间上相对完整和一致性。支持向量机（SVM）是一种基于小样本的学习方法，它避开了从归纳到演绎的传统分类过程。因此，本文提出了基于SVM多光谱LiDAR数据的地物目标分类方法。该方法首先将多个独立波段的LiDAR数据融合为单一的、包含多个波段信息的点云数据，然后将融合后的点云内插为距离影像和多光谱影像，最后利用SVM进行多光谱LiDAR数据的地物覆盖分类。通过对加拿大Optech公司的Titan机载多光谱LiDAR数据的试验证明：相对于传统的单波段LiDAR数据，多光谱LiDAR数据可以获得较好的地物分类精度；比较试验发现SVM分类方法适用于多光谱LiDAR数据的地物分类。
|
| 关 键 词： | 多光谱LiDAR SVM 地物分类 多光谱LiDAR植被指数 |
| 收稿时间： | 2017-09-11 |
| 修稿时间： | 2017-12-04 |
Object Classification Using Airborne Multispectral LiDAR Data |
| |
| PAN Suoyan,GUAN Haiyan.Object Classification Using Airborne Multispectral LiDAR Data[J].Acta Geodaetica et Cartographica Sinica,2018,47(2):198-207. |
| |
| Authors: | PAN Suoyan GUAN Haiyan |
| |
| Institution: | School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China |
| |
| Abstract: | Airborne multispectral LiDAR system,which obtains surface geometry and spectral data of objects,simultaneously,has become a fast effective,large-scale spatial data acquisition method.Multispectral LiDAR data are characteristics of completeness and consistency of spectrum and spatial geometric information.Support vector machine (SVM),a machine learning method,is capable of classifying objects based on small samples.Therefore,by means of SVM,this paper performs land cover classification using multispectral LiDAR data.First,all independent point cloud with different wavelengths are merged into a single point cloud,where each pixel contains the three-wavelength spectral information.Next,the merged point cloud is converted into range and intensity images.Finally,land-cover classification is performed by means of SVM.AII experiments were conducted on the Optech Titan multispectral LiDAR data,containing three individual point cloud collected by 532 nm,1024 nm,and 1550 nm laser beams.Experimental results demonstrate that ①compared to traditional single-wavelength LiDAR data,multispectral LiDAR data provide a promising solution to land use and land cover applications;②SVM is a feasible method for land cover classification of multispectral LiDAR data. |
| |
| Keywords: | |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
| 点击此处可从《测绘学报》浏览原始摘要信息 |
| 点击此处可从《测绘学报》下载免费的PDF全文 |
|
