Acta Geodaetica et Cartographica Sinica >

2018 , Vol. 47 >Issue 2: 198 - 207

DOI: https://doi.org/10.11947/j.AGCS.2018.20170512

Object Classification Using Airborne Multispectral LiDAR Data

  • PAN Suoyan ,
  • GUAN Haiyan
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  • School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China

Received date: 2017-09-11

  Revised date: 2017-12-04

  Online published: 2018-03-02

Supported by

The National Natural Science Foundation of China (No. 41671454)

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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.All 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.

Key words： multispectral LiDAR; SVM; land-cover classification; multispectral lidar-based vegetation indices

Cite this article

PAN Suoyan , GUAN Haiyan . Object Classification Using Airborne Multispectral LiDAR Data[J]. Acta Geodaetica et Cartographica Sinica, 2018 , 47(2) : 198 -207 . DOI: 10.11947/j.AGCS.2018.20170512

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