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Airborne LiDAR point cloud classification using PointNet++ network with full neighborhood features.

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This study introduces an improved deep learning framework for airborne LiDAR point cloud classification. The method enhances accuracy by utilizing adaptive elevation weights and contextual information, outperforming existing techniques.

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Area of Science:

  • Geospatial science
  • Computer vision
  • Remote sensing

Background:

  • Airborne LiDAR point clouds possess unique characteristics not fully exploited by standard deep learning models like PointNet++.
  • Existing methods often suffer from low classification precision due to ignoring these inherent properties and data distribution challenges.

Purpose of the Study:

  • To develop a novel framework for airborne LiDAR point cloud classification that leverages PointNet++.
  • To enhance classification accuracy by incorporating adaptive elevation weighting and contextual information.

Main Methods:

  • An interpolation method with adaptive elevation weights was proposed to better utilize object elevation discrepancies.
  • A class-balanced loss function was implemented to address uneven point cloud density.
  • Multiscale contextual information was captured by densely connecting point pairs and adding centroid features.

Main Results:

  • The proposed method demonstrated high accuracy on the Vaihingen 3D semantic labelling and GML(B) benchmark datasets.
  • The integration of contextual information and adaptive elevation weighting significantly improved classification performance.

Conclusions:

  • The developed framework effectively utilizes airborne LiDAR point cloud properties for improved semantic classification.
  • The method shows potential for wide application in airborne LiDAR point cloud classification tasks.