4.6 Article

A Novel Ground Filtering Method for Point Clouds in a Forestry Area Based on Local Minimum Value and Machine Learning

期刊

APPLIED SCIENCES-BASEL
卷 12, 期 18, 页码 -

出版社

MDPI
DOI: 10.3390/app12189113

关键词

LiDAR point cloud; forest ground filtering method; machine learning

资金

  1. National Key Research and Development Program of China [2018YFB0504500]
  2. National Natural Science Foundation of China [41901295]
  3. Basic ScienceCenter Project of National Natural Science Foundation of China [72088101]
  4. Natural Science Foundation of Hunan Province, China [2020JJ5708]
  5. Key Program of the National Natural Science Foundation of China [41930108]

向作者/读者索取更多资源

This paper focuses on the filtering of point cloud data in forest areas, proposing a new method that combines iterative minima with machine learning to reduce manual intervention, and achieving good filtering results in three different types of woodland areas.
Lidar point cloud filtering is the process of separating ground points from non-ground points and is a particularly important part of point cloud data processing. Forest filtering has always been a difficult topic in point cloud filtering research. Given that vegetation cannot be completely summarized according to the structure of ground objects, and given the diversity and complexity of the terrain in woodland areas, filtering in the forest area is a particularly difficult task. However, only few studies have tested the application of the point cloud filtering method for forest areas, the parameter setting of filtering methods is highly complex, and their terrain adaptability is weak. This paper proposes a new filtering method for forest areas that effectively combines iterative minima with machine learning, thereby greatly reducing the degree of manual participation. Through filtering tests on three types of woodlands, the filtering results were evaluated based on the filtering error definition proposed by ISPRS and were compared with the filtering results of other classical methods. Experimental results highlight the advantages of the proposed method, including its high accuracy, strong terrain universality, and limited number of parameters.

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