Classification and Automatisation of Laser Reflection Points Processing in the Detection of Vegetation
- https://doi.org/10.2991/isees-19.2019.117How to use a DOI?
- laser scanning; classification of cloud of laser points; neural networks.
The article touches upon the issue of automatic recognition and subsequent processing of data on natural and industrial objects of the world. The obtained data is a result of laser scanning. For data analysis in 2018 a research group, consisting of specialists from Kuban State Technological University, Kuban State University, Kuban State Agrarian University and Aerogeomatics Company, conducted a joint research on the decoding of forest cover based on the data of airborne laser scanning under various landscape conditions. The analysis of existing software was performed through the comparison of various methods of automated decoding and the subsequent decoding of points on the basis of airborne laser scanning. Various research results on this topic, including foreign studies, are analyzed. The authors made the conclusions about the quality and reliability of the information provided by each of the products and the level of development of this software segment as a whole. The alternative development options for this industry based on the use of neural networks are given.
- © 2019, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - D.A. Gura AU - M.V. Kuziakina AU - Yu.V. Dubenko AU - S.K. Pshidatok AU - G.G. Shevchenko AU - N.V. Granik AU - I.G. Markovskii PY - 2019/08 DA - 2019/08 TI - Classification and Automatisation of Laser Reflection Points Processing in the Detection of Vegetation BT - Proceedings of the International Symposium "Engineering and Earth Sciences: Applied and Fundamental Research" dedicated to the 85th anniversary of H.I. Ibragimov (ISEES 2019) PB - Atlantis Press SP - 232 EP - 235 SN - 2590-3217 UR - https://doi.org/10.2991/isees-19.2019.117 DO - https://doi.org/10.2991/isees-19.2019.117 ID - Gura2019/08 ER -