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Registration of Aerial Optical Images with LiDAR Data Using the Closest Point Principle and Collinearity Equations

Registration of large-scale optical images with airborne LiDAR data is the basis of the integration of photogrammetry and LiDAR. However, geometric misalignments still exist between some aerial optical images and airborne LiDAR point clouds. To eliminate such misalignments, we extended a method for...

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Detalles Bibliográficos
Autores principales: Huang, Rongyong, Zheng, Shunyi, Hu, Kun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6022127/
https://www.ncbi.nlm.nih.gov/pubmed/29865147
http://dx.doi.org/10.3390/s18061770
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author Huang, Rongyong
Zheng, Shunyi
Hu, Kun
author_facet Huang, Rongyong
Zheng, Shunyi
Hu, Kun
author_sort Huang, Rongyong
collection PubMed
description Registration of large-scale optical images with airborne LiDAR data is the basis of the integration of photogrammetry and LiDAR. However, geometric misalignments still exist between some aerial optical images and airborne LiDAR point clouds. To eliminate such misalignments, we extended a method for registering close-range optical images with terrestrial LiDAR data to a variety of large-scale aerial optical images and airborne LiDAR data. The fundamental principle is to minimize the distances from the photogrammetric matching points to the terrestrial LiDAR data surface. Except for the satisfactory efficiency of about 79 s per 6732 × 8984 image, the experimental results also show that the unit weighted root mean square (RMS) of the image points is able to reach a sub-pixel level (0.45 to 0.62 pixel), and the actual horizontal and vertical accuracy can be greatly improved to a high level of 1/4–1/2 (0.17–0.27 m) and 1/8–1/4 (0.10–0.15 m) of the average LiDAR point distance respectively. Finally, the method is proved to be more accurate, feasible, efficient, and practical in variety of large-scale aerial optical image and LiDAR data.
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spelling pubmed-60221272018-07-02 Registration of Aerial Optical Images with LiDAR Data Using the Closest Point Principle and Collinearity Equations Huang, Rongyong Zheng, Shunyi Hu, Kun Sensors (Basel) Article Registration of large-scale optical images with airborne LiDAR data is the basis of the integration of photogrammetry and LiDAR. However, geometric misalignments still exist between some aerial optical images and airborne LiDAR point clouds. To eliminate such misalignments, we extended a method for registering close-range optical images with terrestrial LiDAR data to a variety of large-scale aerial optical images and airborne LiDAR data. The fundamental principle is to minimize the distances from the photogrammetric matching points to the terrestrial LiDAR data surface. Except for the satisfactory efficiency of about 79 s per 6732 × 8984 image, the experimental results also show that the unit weighted root mean square (RMS) of the image points is able to reach a sub-pixel level (0.45 to 0.62 pixel), and the actual horizontal and vertical accuracy can be greatly improved to a high level of 1/4–1/2 (0.17–0.27 m) and 1/8–1/4 (0.10–0.15 m) of the average LiDAR point distance respectively. Finally, the method is proved to be more accurate, feasible, efficient, and practical in variety of large-scale aerial optical image and LiDAR data. MDPI 2018-06-01 /pmc/articles/PMC6022127/ /pubmed/29865147 http://dx.doi.org/10.3390/s18061770 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Huang, Rongyong
Zheng, Shunyi
Hu, Kun
Registration of Aerial Optical Images with LiDAR Data Using the Closest Point Principle and Collinearity Equations
title Registration of Aerial Optical Images with LiDAR Data Using the Closest Point Principle and Collinearity Equations
title_full Registration of Aerial Optical Images with LiDAR Data Using the Closest Point Principle and Collinearity Equations
title_fullStr Registration of Aerial Optical Images with LiDAR Data Using the Closest Point Principle and Collinearity Equations
title_full_unstemmed Registration of Aerial Optical Images with LiDAR Data Using the Closest Point Principle and Collinearity Equations
title_short Registration of Aerial Optical Images with LiDAR Data Using the Closest Point Principle and Collinearity Equations
title_sort registration of aerial optical images with lidar data using the closest point principle and collinearity equations
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6022127/
https://www.ncbi.nlm.nih.gov/pubmed/29865147
http://dx.doi.org/10.3390/s18061770
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