The influence of flight height and overlap on UAV imagery over featureless surfaces and constructing formulas predicting the geometrical accuracy Article Swipe
Ahmed Elhadary
,
Mostafa Rabah
,
Essam Ghanim
,
Rasha Mohie
,
Ahmed Taha
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1080/20909977.2022.2057148
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1080/20909977.2022.2057148
The improvement of unmanned aerial system and photogrammetric computer vision (CV) algorithms has presented an aerial imaging technique for high accuracy and low-cost alternatives for mapping and topographic applications. Structure from motion (SFM) is an automation photogrammetric CV algorithm used for generating 3D coloured point clouds and 3D models from overlapping images. One of the biggest problems preventing the automation extraction and matching key points in the aligning aerial images is the non-texture of the covered area surface. This paper assessed the effect of flight altitude and overlap degree on 3D point clouds' geometric accuracy and models produced by unmanned aerial vehicle (UAV) images captured over non-textured sandy areas. Four flight altitudes (140, 160, 180 and 200 m) related to spatial resolution (3.41, 3.9, 4.39 and 4.68 cm/pix ground sample distance (GSD)), respectively, and three overlap levels (60%, 70% and 80%) were assessed using RGB images captured by UX5 UAV over a non-textured sandy area in Jahra, Kuwait. The results showed that altitude increment might reduce flight time, processing time and cost, keeping with the acceptable and suitable geometric accuracy. Generally, favourable results are obtained for the four altitudes and overlap degrees of 80% at least. Multivariate nonlinear regression analysis was used to fit the relation between geometric accuracy, image overlap and GSD cm/pixel for the seven missions determining two formulas that predict the geometrical accuracy of the UAV point cloud with a precision of 92.76% and 91.91% for both formulas.
Related Topics
Concepts
Photogrammetry
Artificial intelligence
Computer vision
Point cloud
Remote sensing
Ground sample distance
Pixel
RGB color model
Altitude (triangle)
Computer science
Structure from motion
Automation
Point (geometry)
Matching (statistics)
Mathematics
Geography
Geometry
Motion (physics)
Statistics
Engineering
Mechanical engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1080/20909977.2022.2057148
- https://www.tandfonline.com/doi/pdf/10.1080/20909977.2022.2057148?needAccess=true
- OA Status
- diamond
- Cited By
- 25
- References
- 10
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4225357243
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4225357243Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1080/20909977.2022.2057148Digital Object Identifier
- Title
-
The influence of flight height and overlap on UAV imagery over featureless surfaces and constructing formulas predicting the geometrical accuracyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-04-04Full publication date if available
- Authors
-
Ahmed Elhadary, Mostafa Rabah, Essam Ghanim, Rasha Mohie, Ahmed TahaList of authors in order
- Landing page
-
https://doi.org/10.1080/20909977.2022.2057148Publisher landing page
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https://www.tandfonline.com/doi/pdf/10.1080/20909977.2022.2057148?needAccess=trueDirect link to full text PDF
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
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https://www.tandfonline.com/doi/pdf/10.1080/20909977.2022.2057148?needAccess=trueDirect OA link when available
- Concepts
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Photogrammetry, Artificial intelligence, Computer vision, Point cloud, Remote sensing, Ground sample distance, Pixel, RGB color model, Altitude (triangle), Computer science, Structure from motion, Automation, Point (geometry), Matching (statistics), Mathematics, Geography, Geometry, Motion (physics), Statistics, Engineering, Mechanical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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25Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 8, 2024: 13, 2023: 4Per-year citation counts (last 5 years)
- References (count)
-
10Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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