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isprs-archives-XLIII-B2-2020-375-2020.pdf
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📊 Size: 1.7 MB
📄 Pages: 8 pages
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Summary

Airborne LiDAR point clouds and images data fusion have been widely studied, with recent developments in photogrammetric technology allowing images to provide dense image matching (DIM) point clouds. A sample selection framework is introduced to classify LiDAR points using DIM points as auxiliary data, with a multi-class TrAdaboost algorithm automatically selecting useful samples from DIM points to improve LiDAR points classification performance. The framework only selects useful features from DIM points, eliminating the need for registration between DIM and LiDAR points, and can utilize images containing classes in LiDAR point clouds. Experiments show that the framework improves LiDAR points classification performance, with better quality DIM points resulting in better classification performance.

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Airborne LiDAR point clouds and images data fusion have been widely studied, with recent developments in photogrammetric technology allowing images to provide...

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  • File Format: PDF
  • File Size: 1.7 MB
  • Pages: 8
  • Language: EN
  • Total Downloads: 219
  • Last Updated: 4 hours ago

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