A SPECTRALLY IMPROVED POINT CLOUD CLASSIFICATION METHOD FOR MULTISPECTRAL LIDAR
Precise point cloud classification can enhance lidar performance in various applications, such as land cover mapping, forestry management and autonomous driving. The development of multispectral lidar improves classification performance with rich spectral information. However, the employment of spec...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2020-08-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2020/501/2020/isprs-archives-XLIII-B3-2020-501-2020.pdf |
| _version_ | 1828825717489532928 |
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| author | B. Chen S. Shi W. Gong J. Sun B. Chen K. Guo L. Du J. Yang Q. Xu S. Song |
| author_facet | B. Chen S. Shi W. Gong J. Sun B. Chen K. Guo L. Du J. Yang Q. Xu S. Song |
| author_sort | B. Chen |
| collection | DOAJ |
| description | Precise point cloud classification can enhance lidar performance in various applications, such as land cover mapping, forestry management and autonomous driving. The development of multispectral lidar improves classification performance with rich spectral information. However, the employment of spectral information for classification is still underdeveloped. Therefore, we proposed a spectrally improved classification method for multispectral LiDAR. We conducted spectral improvement in two aspects: (1) we improved the eigenentropy-based neighbourhood selection by spectral angle match (SAM) to reform the more reliable neighbour; (2) we utilized both geometric and spectral features and compare the contributions of these features. A three-wavelength multispectral lidar and a complex indoor experimental scene were used for demonstration. The results indicate the effectiveness of our proposed spectrally improved method and the promising potential of spectral information on lidar classification. |
| first_indexed | 2024-12-12T14:24:13Z |
| format | Article |
| id | doaj.art-8cc2213810e54a40a941dc3e296e811f |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-12-12T14:24:13Z |
| publishDate | 2020-08-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-8cc2213810e54a40a941dc3e296e811f2022-12-22T00:21:44ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342020-08-01XLIII-B3-202050150510.5194/isprs-archives-XLIII-B3-2020-501-2020A SPECTRALLY IMPROVED POINT CLOUD CLASSIFICATION METHOD FOR MULTISPECTRAL LIDARB. Chen0S. Shi1W. Gong2J. Sun3B. Chen4K. Guo5L. Du6J. Yang7Q. Xu8S. Song9State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaFaculty of Information Engineering, China University of Geosciences, Wuhan, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaFaculty of Information Engineering, China University of Geosciences, Wuhan, ChinaFaculty of Information Engineering, China University of Geosciences, Wuhan, ChinaFaculty of Information Engineering, China University of Geosciences, Wuhan, ChinaState Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, ChinaPrecise point cloud classification can enhance lidar performance in various applications, such as land cover mapping, forestry management and autonomous driving. The development of multispectral lidar improves classification performance with rich spectral information. However, the employment of spectral information for classification is still underdeveloped. Therefore, we proposed a spectrally improved classification method for multispectral LiDAR. We conducted spectral improvement in two aspects: (1) we improved the eigenentropy-based neighbourhood selection by spectral angle match (SAM) to reform the more reliable neighbour; (2) we utilized both geometric and spectral features and compare the contributions of these features. A three-wavelength multispectral lidar and a complex indoor experimental scene were used for demonstration. The results indicate the effectiveness of our proposed spectrally improved method and the promising potential of spectral information on lidar classification.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2020/501/2020/isprs-archives-XLIII-B3-2020-501-2020.pdf |
| spellingShingle | B. Chen S. Shi W. Gong J. Sun B. Chen K. Guo L. Du J. Yang Q. Xu S. Song A SPECTRALLY IMPROVED POINT CLOUD CLASSIFICATION METHOD FOR MULTISPECTRAL LIDAR The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | A SPECTRALLY IMPROVED POINT CLOUD CLASSIFICATION METHOD FOR MULTISPECTRAL LIDAR |
| title_full | A SPECTRALLY IMPROVED POINT CLOUD CLASSIFICATION METHOD FOR MULTISPECTRAL LIDAR |
| title_fullStr | A SPECTRALLY IMPROVED POINT CLOUD CLASSIFICATION METHOD FOR MULTISPECTRAL LIDAR |
| title_full_unstemmed | A SPECTRALLY IMPROVED POINT CLOUD CLASSIFICATION METHOD FOR MULTISPECTRAL LIDAR |
| title_short | A SPECTRALLY IMPROVED POINT CLOUD CLASSIFICATION METHOD FOR MULTISPECTRAL LIDAR |
| title_sort | spectrally improved point cloud classification method for multispectral lidar |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2020/501/2020/isprs-archives-XLIII-B3-2020-501-2020.pdf |
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