Archives
 
Year 2022
 
Vol 63, Issue 5, [10 - 2022]

LiDAR point cloud classification using point elevation and reflection intensity

DOI:10.46326/JMES.2022.63(5).05

  • Affiliations:

    1 Hanoi Univeristy of Mining and Geology, Hanoi, Vietnam
    2 Institute of Information Technology - VAST, Hanoi, Vietnam
    3 Scientific and technical development support center - HUMG, Hanoi, Vietnam
    4 General Department of Geology and Minerals – MONRE, Hanoi, Vietnam

  • *Corresponding:
    This email address is being protected from spambots. You need JavaScript enabled to view it.
  • Received: 9th-June-2022
  • Revised: 29th-Sept-2022
  • Accepted: 4th-Oct-2022
  • Online: 31st-Oct-2022
Pages: 48 - 57
Views: 3029
View onlineView online
Downloads: 2114
Downdload PDF
★★★★★Rating: 5.0, Total rating: 211
  • ★
  • ★
  • ★
  • ★
  • ★
Yours rating

Abstract:

The data obtained from LiDAR includes a lot of valuable information and is applied in many different fields such as geodesy - cartography, and antiques. information transmission, etc. LiDAR point cloud contains a lot of information about the object such as high point, point reflection intensity, nominal distance (NPS), and grayscale value, etc., each information is used in different problems. to clarify the three-dimensional spatial distribution, the zoning surface, or the features of the topography and features in the survey area. In the article, the authors use information altitude and reflection intensity, two typical symbols of data LiDAR, to implement a mathematical layer application to set digital elevation (DEM), model the face number (DSM), and 3D model to verify the partition of address, address at the area of testing. Pitch information is used by the author to separate groups of ground (ground) and non-ground (non-ground) points. Value reflection will be used to enhance accuracy when performing groundless classification into vegetative strata, and tall buildings. The use of point intensity reflection enhances the accuracy of previous high point-based geometry processing methods. With the accuracy of the problem analysis class reaching (ground (93.8%), building (91%), and vegetation (93,7%)), the models are set up just out of the partition of the required answer surface of the problem application.

How to Cite
Nguyen, P.Huu Thi, Dang, D.Van, Nguyen, X.Truong, Pham, L.Huu and Nguyen, T.Minh 2022. LiDAR point cloud classification using point elevation and reflection intensity (in Vietnamese). Journal of Mining and Earth Sciences. 63, 5 (Oct, 2022), 48-57. DOI:https://doi.org/10.46326/JMES.2022.63(5).05.
References

Dong, P. and Chen, Q. (2017). LiDAR remote sensing and applications. CRC Press.

ESRI, (2016). ArcGIS for Desktop. http://desktop.arcgis.com/en/arcmap/10.3/manage-data/las-dataset/what-is-lidar-data-.html.

Gao, Y. and Li, M.C., (2020). Airborne lidar point cloud classification based on multilevel point cluster features. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 1231-1237.

Geodetics, (2020). LiDAR intensity: What is it and What are it's application?. https://geodetics.com/lidar-intensity-applications/.

Jing, Z., Guan, H., Zhao, P., Li, D., Yu, Y., Zang, Y., Wang, H. and Li, J., (2021). Multispectral LiDAR point cloud classification using SE-PointNet++. Remote Sensing, 13(13), 2516.

Kalantari, B., (2013). The State of the Art of Voronoi Diagram Research. Transactions on Computational Science XX, 1-4.

Lin, C.C., Mao, W.L. and Hu, T.L., (2020). Point Cloud Registration Using Intensity Features. Sensors and Materials, 32(7), 2355-2364.

Lin, K.F., Wang, C.P., and Sui, P.H., (2012). Object-Based Classification for LiDAR Point Cloud. Sematicscholars. https://pdfs. semanticscholar. org/ea05/a9 226252f933470a88fa73a3150802ad08e3. pdf.

Neonscience, (2020). The Basic of LiDAR. https://www.neonscience.org/resources/learning-hub/tutorials/lidar-basics.

Pokojski, W., and Pokojska, P., (2018). Voronoi diagrams–inventor, method, applications. Polish Cartographical Review, 50(3), 141-150.

Rodríguez-Cuenca, B., García-Cortés, S., Ordóñez, C., and Alonso, M.C., (2015). Automatic detection and classification of pole-like objects in urban point cloud data using an anomaly detection algorithm. Remote Sensing, 7(10), 12680-12703.

Rodriguez-Perez, R., Vogt, M., and Bajorath, J., (2017). Support vector machine classification and regression prioritize different structural features for binary compound activity and potency value prediction. ACS omega, 2(10), 6371-6379.

Vladutescu, V., (2018). Lidar system and working principles. Application in atmospheric monitoring, surveillance and metrology. New York City College of Technology. New York.

Yunfei, B., Guoping, L., Chunxiang, C., Xiaowen, L., Hao, Z., Qisheng, H., Linyan, B. and Chaoyi, C., (2008). Classification of LIDAR point cloud and generation of DTM from LIDAR height and intensity data in forested area. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(7), 313-318.

Wasser, L.A., (2020). The Basics of LiDAR-Light Detection and Ranging-Remote Sensing| NSF NEON| Open Data to Understand our Ecosystems.