Machine learning-based optimization of photogrammetric JRC accuracy

Machine learning-based optimization of photogrammetric JRC accuracy

Abstract To improve the accuracy of photogrammetric joint roughness coefficient (JRC) estimation, this study proposes two optimization models based on ground sample distance (GSD), point density, and the root mean square error (RMSE) of checkpoints. First, an algorithm that automatically generates s...

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Bibliographic Details
Main Authors: Qinzheng Yang, Ang Li, Yipeng Liu, Hongtian Wang, Zhendong Leng, Fei Deng
Format: Article
Language:English
Published: Nature Portfolio 2024-11-01
Series:Scientific Reports
Subjects:
Photogrammetry
Joint roughness coefficient
JRC optimization
3D reconstruction
Online Access:https://doi.org/10.1038/s41598-024-77054-w
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https://doi.org/10.1038/s41598-024-77054-w

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