Cargando…
Investigating the Potential of Using the Spatial and Spectral Information of Multispectral LiDAR for Object Classification
The abilities of multispectral LiDAR (MSL) as a new high-potential active instrument for remote sensing have not been fully revealed. This study demonstrates the potential of using the spectral and spatial features derived from a novel MSL to discriminate surface objects. Data acquired with the MSL...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4610482/ https://www.ncbi.nlm.nih.gov/pubmed/26340630 http://dx.doi.org/10.3390/s150921989 |
_version_ | 1782395947069734912 |
---|---|
author | Gong, Wei Sun, Jia Shi, Shuo Yang, Jian Du, Lin Zhu, Bo Song, Shalei |
author_facet | Gong, Wei Sun, Jia Shi, Shuo Yang, Jian Du, Lin Zhu, Bo Song, Shalei |
author_sort | Gong, Wei |
collection | PubMed |
description | The abilities of multispectral LiDAR (MSL) as a new high-potential active instrument for remote sensing have not been fully revealed. This study demonstrates the potential of using the spectral and spatial features derived from a novel MSL to discriminate surface objects. Data acquired with the MSL include distance information and the intensities of four wavelengths at 556, 670, 700, and 780 nm channels. A support vector machine was used to classify diverse objects in the experimental scene into seven types: wall, ceramic pots, Cactaceae, carton, plastic foam block, and healthy and dead leaves of E. aureum. Different features were used during classification to compare the performance of different detection systems. The spectral backscattered reflectance of one wavelength and distance represented the features from an equivalent single-wavelength LiDAR system; reflectance of the four wavelengths represented the features from an equivalent multispectral image with four bands. Results showed that the overall accuracy of using MSL data was as high as 88.7%, this value was 9.8%–39.2% higher than those obtained using a single-wavelength LiDAR, and 4.2% higher than for multispectral image. |
format | Online Article Text |
id | pubmed-4610482 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-46104822015-10-26 Investigating the Potential of Using the Spatial and Spectral Information of Multispectral LiDAR for Object Classification Gong, Wei Sun, Jia Shi, Shuo Yang, Jian Du, Lin Zhu, Bo Song, Shalei Sensors (Basel) Article The abilities of multispectral LiDAR (MSL) as a new high-potential active instrument for remote sensing have not been fully revealed. This study demonstrates the potential of using the spectral and spatial features derived from a novel MSL to discriminate surface objects. Data acquired with the MSL include distance information and the intensities of four wavelengths at 556, 670, 700, and 780 nm channels. A support vector machine was used to classify diverse objects in the experimental scene into seven types: wall, ceramic pots, Cactaceae, carton, plastic foam block, and healthy and dead leaves of E. aureum. Different features were used during classification to compare the performance of different detection systems. The spectral backscattered reflectance of one wavelength and distance represented the features from an equivalent single-wavelength LiDAR system; reflectance of the four wavelengths represented the features from an equivalent multispectral image with four bands. Results showed that the overall accuracy of using MSL data was as high as 88.7%, this value was 9.8%–39.2% higher than those obtained using a single-wavelength LiDAR, and 4.2% higher than for multispectral image. MDPI 2015-09-02 /pmc/articles/PMC4610482/ /pubmed/26340630 http://dx.doi.org/10.3390/s150921989 Text en © 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Gong, Wei Sun, Jia Shi, Shuo Yang, Jian Du, Lin Zhu, Bo Song, Shalei Investigating the Potential of Using the Spatial and Spectral Information of Multispectral LiDAR for Object Classification |
title | Investigating the Potential of Using the Spatial and Spectral Information of Multispectral LiDAR for Object Classification |
title_full | Investigating the Potential of Using the Spatial and Spectral Information of Multispectral LiDAR for Object Classification |
title_fullStr | Investigating the Potential of Using the Spatial and Spectral Information of Multispectral LiDAR for Object Classification |
title_full_unstemmed | Investigating the Potential of Using the Spatial and Spectral Information of Multispectral LiDAR for Object Classification |
title_short | Investigating the Potential of Using the Spatial and Spectral Information of Multispectral LiDAR for Object Classification |
title_sort | investigating the potential of using the spatial and spectral information of multispectral lidar for object classification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4610482/ https://www.ncbi.nlm.nih.gov/pubmed/26340630 http://dx.doi.org/10.3390/s150921989 |
work_keys_str_mv | AT gongwei investigatingthepotentialofusingthespatialandspectralinformationofmultispectrallidarforobjectclassification AT sunjia investigatingthepotentialofusingthespatialandspectralinformationofmultispectrallidarforobjectclassification AT shishuo investigatingthepotentialofusingthespatialandspectralinformationofmultispectrallidarforobjectclassification AT yangjian investigatingthepotentialofusingthespatialandspectralinformationofmultispectrallidarforobjectclassification AT dulin investigatingthepotentialofusingthespatialandspectralinformationofmultispectrallidarforobjectclassification AT zhubo investigatingthepotentialofusingthespatialandspectralinformationofmultispectrallidarforobjectclassification AT songshalei investigatingthepotentialofusingthespatialandspectralinformationofmultispectrallidarforobjectclassification |