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An Improved DBSCAN Method for LiDAR Data Segmentation with Automatic Eps Estimation.

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This study introduces an automatic parameter estimation method for density-based spatial clustering of applications with noise (DBSCAN) to improve Light Detection and Ranging (LiDAR) point cloud segmentation. The novel approach enhances segmentation accuracy for diverse LiDAR datasets.

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Area of Science:

  • Geospatial data analysis
  • Computer vision
  • Machine learning

Background:

  • Point cloud data processing involves segmentation, filtering, classification, and feature extraction.
  • Density-based spatial clustering of applications with noise (DBSCAN) is effective for segmenting Light Detection and Ranging (LiDAR) data due to its ability to identify arbitrary cluster shapes.
  • A key challenge in applying DBSCAN to point cloud data is the difficulty in determining the optimal searching radius parameter (ε).

Purpose of the Study:

  • To propose a novel automatic parameter estimation method for DBSCAN to improve LiDAR point cloud segmentation.
  • To address the challenge of determining the optimal searching radius (ε) for DBSCAN in point cloud processing.
  • To enhance the accuracy and robustness of LiDAR point cloud segmentation using an automated approach.

Main Methods:

  • Developed an automatic ε estimation method based on the average of k-nearest neighbors' maximum distance.
  • The method utilizes the fitting curve of k and the mean maximum distance to calculate ε from point cloud intrinsic properties.
  • Evaluated the proposed algorithm on airborne and mobile LiDAR point cloud data, with and without color information.

Main Results:

  • The proposed automatic ε estimation method achieved segmentation accuracy values of 75% (airborne), 74% (mobile, with color), and 71% (mobile, without color).
  • These accuracy values are higher than those obtained using manually selected smaller or larger ε parameters.
  • The algorithm demonstrated robust performance across different types of LiDAR point clouds.

Conclusions:

  • The novel automatic parameter estimation method significantly improves the accuracy of DBSCAN-based LiDAR point cloud segmentation.
  • The proposed algorithm is robust and effective for processing both airborne and mobile LiDAR data.
  • This automated approach can reduce manual effort and enhance the efficiency of LiDAR data processing systems.