The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 501–505, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-501-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 501–505, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-501-2020

  21 Aug 2020

21 Aug 2020

A SPECTRALLY IMPROVED POINT CLOUD CLASSIFICATION METHOD FOR MULTISPECTRAL LIDAR

B. Chen1, S. Shi1, W. Gong1, J. Sun2, B. Chen1, K. Guo1, L. Du2, J. Yang2, Q. Xu2, and S. Song3 B. Chen et al.
  • 1State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
  • 2Faculty of Information Engineering, China University of Geosciences, Wuhan, China
  • 3State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, China

Keywords: Multispectral Lidar, Point Cloud Classification, Neighborhood Selection, Feature Extraction, Condition Random Field

Abstract. 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.