Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Styliani Verykokou
 -- 461 2024-02-02 06:23:39 |
2 layout
Camila Xu
 Meta information modification 461 2024-02-02 06:25:55 |

Video Upload Options

We provide professional Video Production Services to translate complex research into visually appealing presentations. Would you like to try it?
No, upload directly Yes

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Verykokou, S.; Ioannidis, C. Oblique Aerial Images: Geometric Principles, Relationships and Definitions. Encyclopedia. Available online: https://encyclopedia.pub/entry/54663 (accessed on 22 December 2024).
Verykokou S, Ioannidis C. Oblique Aerial Images: Geometric Principles, Relationships and Definitions. Encyclopedia. Available at: https://encyclopedia.pub/entry/54663. Accessed December 22, 2024.
Verykokou, Styliani, Charalabos Ioannidis. "Oblique Aerial Images: Geometric Principles, Relationships and Definitions" Encyclopedia, https://encyclopedia.pub/entry/54663 (accessed December 22, 2024).
Verykokou, S., & Ioannidis, C. (2024, February 02). Oblique Aerial Images: Geometric Principles, Relationships and Definitions. In Encyclopedia. https://encyclopedia.pub/entry/54663
Verykokou, Styliani and Charalabos Ioannidis. "Oblique Aerial Images: Geometric Principles, Relationships and Definitions." Encyclopedia. Web. 02 February, 2024.
Peer Reviewed
Oblique Aerial Images: Geometric Principles, Relationships and Definitions
This is a peer-reviewed entry published in Encyclopedia Journal, full entry can be found at 10.3390/encyclopedia4010019

Aerial images captured with the camera optical axis deliberately inclined with respect to the vertical are defined as oblique aerial images. Throughout the evolution of aerial photography, oblique aerial images have held a prominent place since its inception. While vertical airborne images dominated in photogrammetric applications for over a century, the advancements in photogrammetry and computer vision algorithms, coupled with the growing accessibility of oblique images in the market, have propelled the rise of oblique images in recent times. Their emergence is attributed to inherent advantages they offer over vertical images. In this entry, basic definitions, geometric principles and relationships for oblique aerial images, necessary for understanding their underlying geometry, are presented.

oblique aerial images oblique imagery photogrammetry image geometry image scale image tilt nadir point horizon point isocenter camera configurations
Over the past twenty years, there has been a notable rise in the utilization of datasets featuring oblique aerial imagery. This surge can be attributed to advancements in photogrammetric and computer vision algorithms, mainly concerning image-based 3D reconstruction methods [1], the growing accessibility of these images in the market and their inherent advantages over vertical imagery. Specifically, oblique imagery offers the depiction of both vertical structures, predominantly facades, alongside horizontal elements, mimicking the human perception of scenes from a ground-level view, thus enhancing the portrayal of landscapes. Both the scientific community and multiple companies have been utilizing oblique images for diverse applications, leading to significant advancements in their automated processing [2][3]. Applications utilizing oblique aerial images include—but are not limited to—image matching [4][5][6], georeferencing [3][7][8][9], orientation and structure from motion procedures [10][11][12][13][14][15], multi-view stereo and 3D modeling pipelines [16][17][18][19][20], texture mapping [21][22][23], object detection [24][25], building identification [26][27][28], semantic segmentation of 3D city models [29] and buildings [30][31], building classification [32], extraction of post-disaster structural damages [33][34][35], historic building information modeling (HBIM) [36], reconstruction of LoD-2 building models [37], cadastral mapping [38], 3D reconstruction of canopy [39] and estimation of canopy height [40], moving car recognition [41], animal detection [42], and river surface ice quantification [43].
In this entry, basic definitions and geometric relationships for oblique aerial images, necessary for understanding their underlying geometry, are presented. The entry starts with the definition of the types of oblique aerial images and the presentation of corresponding camera setups. Subsequently, terms associated with the geometry of oblique aerial images are defined, the angular orientation in terms of azimuth, tilt and swing is presented, the displacement due to tilt is defined, and the combined effect of displacements due to relief and tilt is presented. Additionally, formulas for estimation of scales (x-scale, y-scale) in an oblique aerial image are presented, and some basic geometrical relationships among characteristic image points and angles of oblique aerial images are defined. The entry ends with the presentation of mathematical relationships that can be used for determining vertical as well as horizontal distances from a single oblique aerial image.

References

  1. Verykokou, S.; Ioannidis, C. An Overview on Image-Based and Scanner-Based 3D Modeling Technologies. Sensors 2023, 23, 596.
  2. Verykokou, S.; Ioannidis, C. Oblique Aerial Images: A Review Focusing on Georeferencing Procedures. Int. J. Remote Sens. 2018, 39, 3452–3496.
  3. Verykokou, S.A. Georeferencing Procedures for Oblique Aerial Images. Ph.D. Thesis, National Technical University of Athens, Athens, Greece, 2020.
  4. Hu, H.; Ding, Y.; Zhu, Q.; Wu, B.; Xie, L.; Chen, M. Stable Least-Squares Matching for Oblique Images Using Bound Constrained Optimization and a Robust Loss Function. J. Photogramm. Remote Sens. 2016, 118, 53–67.
  5. Wang, C.; Chen, J.; Chen, J.; Yue, A.; He, D.; Huang, Q.; Zhang, Y. Unmanned Aerial Vehicle Oblique Image Registration Using an ASIFT-Based Matching Method. J. Appl. Remote Sens. 2018, 12, 025002.
  6. Zhang, Q.; Zheng, S.; Zhang, C.; Wang, X.; Li, R. Efficient Large-Scale Oblique Image Matching Based on Cascade Hashing and Match Data Scheduling. Pattern Recognit. 2023, 138, 109442.
  7. Zhao, H.; Zhang, B.; Wu, C.; Zuo, Z.; Chen, Z.; Bi, J. Direct Georeferencing of Oblique and Vertical Imagery in Different Coordinate Systems. J. Photogramm. Remote Sens. 2014, 95, 122–133.
  8. Geniviva, A.; Faulring, J.; Salvaggio, C. Automatic Georeferencing of Imagery from High-Resolution, Low-Altitude, Low-Cost Aerial Platforms. In Proceedings of the Geospatial InfoFusion and Video Analytics IV; and Motion Imagery for ISR and Situational Awareness II, Baltimore, MD, USA, 19 June 2014; Volume 9089, pp. 101–109.
  9. Verykokou, S.; Ioannidis, C. Automatic Rough Georeferencing of Multiview Oblique and Vertical Aerial Image Datasets of Urban Scenes. Photogramm. Rec. 2016, 31, 281–303.
  10. Xie, L.; Hu, H.; Wang, J.; Zhu, Q.; Chen, M. An Asymmetric Re-Weighting Method for the Precision Combined Bundle Adjustment of Aerial Oblique Images. J. Photogramm. Remote Sens. 2016, 117, 92–107.
  11. Verykokou, S.; Ioannidis, C. A Photogrammetry-Based Structure from Motion Algorithm Using Robust Iterative Bundle Adjustment Techniques. Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2018, 4, 73–80.
  12. Verykokou, S.; Ioannidis, C. A Global Photogrammetry-Based Structure from Motion Framework: Application in Oblique Aerial Images. In Proceedings of the FIG Working Week 2019, Hanoi, Vietnam, 22–26 April 2019; FIG: Hanoi, Vietnam, 2019.
  13. Verykokou, S.; Ioannidis, C. Exterior Orientation Estimation of Oblique Aerial Images Using SfM-Based Robust Bundle Adjustment. Int. J. Remote Sens. 2020, 41, 7233–7270.
  14. Jiang, S.; Jiang, C.; Jiang, W. Efficient Structure from Motion for Large-Scale UAV Images: A Review and a Comparison of SfM Tools. J. Photogramm. Remote Sens. 2020, 167, 230–251.
  15. Liang, Y.; Yang, Y.; Fan, X.; Cui, T. Efficient and Accurate Hierarchical SfM Based on Adaptive Track Selection for Large-Scale Oblique Images. Remote Sens. 2023, 15, 1374.
  16. Wu, B.; Xie, L.; Hu, H.; Zhu, Q.; Yau, E. Integration of Aerial Oblique Imagery and Terrestrial Imagery for Optimized 3D Modeling in Urban Areas. J. Photogramm. Remote Sens. 2018, 139, 119–132.
  17. Nesbit, P.R.; Hugenholtz, C.H. Enhancing UAV–SfM 3D Model Accuracy in High-Relief Landscapes by Incorporating Oblique Images. Remote Sens. 2019, 11, 239.
  18. Liu, J.; Zhang, L.; Wang, Z.; Wang, R. Dense Stereo Matching Strategy for Oblique Images That Considers the Plane Directions in Urban Areas. IEEE Trans. Geosci. Remote Sens. 2020, 58, 5109–5116.
  19. Pepe, M.; Fregonese, L.; Crocetto, N. Use of SfM-MVS Approach to Nadir and Oblique Images Generated Throught Aerial Cameras to Build 2.5D Map and 3D Models in Urban Areas. Geocarto Int. 2022, 37, 120–141.
  20. Oniga, V.-E.; Breaban, A.-I.; Pfeifer, N.; Diac, M. 3D Modeling of Urban Area Based on Oblique UAS Images—An End-to-End Pipeline. Remote Sens. 2022, 14, 422.
  21. Frommholz, D.; Linkiewicz, M.; Meissner, H.; Dahlke, D.; Poznanska, A. Extracting Semantically Annotated 3DBuilding Models with Textures from Oblique Aerial Imagery. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2015, 40, 53–58.
  22. Kang, J.; Deng, F.; Li, X.; Wan, F. Automatic Texture Reconstruction of 3D City Model from Oblique Images. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2016, 41, 341–347.
  23. Zhou, G.; Bao, X.; Ye, S.; Wang, H.; Yan, H. Selection of Optimal Building Facade Texture Images From UAV-Based Multiple Oblique Image Flows. IEEE Trans. Geosci. Remote Sens. 2021, 59, 1534–1552.
  24. Shen, H.; Lin, D.; Song, T. Object Detection Deployed on UAVs for Oblique Images by Fusing IMU Information. IEEE Geosci. Remote Sens. Lett. 2022, 19, 6505305.
  25. Zachar, P.; Kurczyński, Z.; Ostrowski, W. Application of Machine Learning for Object Detection in Oblique Aerial Images. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2022, 43, 657–663.
  26. Cai, Y.; Ding, Y.; Zhang, H.; Xiu, J.; Liu, Z. Geo-Location Algorithm for Building Targets in Oblique Remote Sensing Images Based on Deep Learning and Height Estimation. Remote Sens. 2020, 12, 2427.
  27. Zhang, L.; Wang, G.; Sun, W. Automatic Identification of Building Structure Types Using Unmanned Aerial Vehicle Oblique Images and Deep Learning Considering Facade Prior Knowledge. Int. J. Digit. Earth 2023, 16, 3348–3367.
  28. Liang, Y.; Fan, X.; Yang, Y.; Li, D.; Cui, T. Oblique View Selection for Efficient and Accurate Building Reconstruction in Rural Areas Using Large-Scale UAV Images. Drones 2022, 6, 175.
  29. Wilk, Ł.; Mielczarek, D.; Ostrowski, W.; Dominik, W.; Krawczyk, J. Semantic Urban Mesh Segmentation Based on Aerial Oblique Images and Point Clouds Using Deep Learning. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2022, 43, 485–491.
  30. Khoshboresh-Masouleh, M.; Alidoost, F.; Arefi, H. Multiscale Building Segmentation Based on Deep Learning for Remote Sensing RGB Images from Different Sensors. J. Appl. Remote Sens. 2020, 14, 034503.
  31. Mao, Z.; Huang, X.; Gong, Y.; Xiang, H.; Zhang, F. A Dataset and Ensemble Model for Glass Façade Segmentation in Oblique Aerial Images. IEEE Geosci. Remote Sens. Lett. 2022, 19, 6513305.
  32. Meng, C.; Song, Y.; Ji, J.; Jia, Z.; Zhou, Z.; Gao, P.; Liu, S. Automatic Classification of Rural Building Characteristics Using Deep Learning Methods on Oblique Photography. Build. Simul. 2022, 15, 1161–1174.
  33. Vetrivel, A.; Gerke, M.; Kerle, N.; Vosselman, G. Identification of Structurally Damaged Areas in Airborne Oblique Images Using a Visual-Bag-of-Words Approach. Remote Sens. 2016, 8, 231.
  34. Kakooei, M.; Baleghi, Y. A Two-Level Fusion for Building Irregularity Detection in Post-Disaster VHR Oblique Images. Earth Sci. Inf. 2020, 13, 459–477.
  35. Zhang, R.; Li, H.; Duan, K.; You, S.; Liu, K.; Wang, F.; Hu, Y. Automatic Detection of Earthquake-Damaged Buildings by Integrating UAV Oblique Photography and Infrared Thermal Imaging. Remote Sens. 2020, 12, 2621.
  36. Martínez-Carricondo, P.; Carvajal-Ramírez, F.; Yero-Paneque, L.; Agüera-Vega, F. Combination of Nadiral and Oblique UAV Photogrammetry and HBIM for the Virtual Reconstruction of Cultural Heritage. Case Study of Cortijo Del Fraile in Níjar, Almería (Spain). Build. Res. Inf. 2020, 48, 140–159.
  37. Wang, F.; Zhou, G.; Hu, H.; Wang, Y.; Fu, B.; Li, S.; Xie, J. Reconstruction of LoD-2 Building Models Guided by Façade Structures from Oblique Photogrammetric Point Cloud. Remote Sens. 2023, 15, 400.
  38. Šafář, V.; Potůčková, M.; Karas, J.; Tlustý, J.; Štefanová, E.; Jančovič, M.; Cígler Žofková, D. The Use of UAV in Cadastral Mapping of the Czech Republic. ISPRS Int. J. Geo-Inf. 2021, 10, 380.
  39. Díaz, G.M.; Mohr-Bell, D.; Garrett, M.; Muñoz, L.; Lencinas, J.D. Customizing Unmanned Aircraft Systems to Reduce Forest Inventory Costs: Can Oblique Images Substantially Improve the 3D Reconstruction of the Canopy? Int. J. Remote Sens. 2020, 41, 3480–3510.
  40. Li, M.; Shamshiri, R.R.; Schirrmann, M.; Weltzien, C.; Shafian, S.; Laursen, M.S. UAV Oblique Imagery with an Adaptive Micro-Terrain Model for Estimation of Leaf Area Index and Height of Maize Canopy from 3D Point Clouds. Remote Sens. 2022, 14, 585.
  41. Yang, C.; Zhang, F.; Gao, Y.; Mao, Z.; Li, L.; Huang, X. Moving Car Recognition and Removal for 3D Urban Modelling Using Oblique Images. Remote Sens. 2021, 13, 3458.
  42. Lamprey, R.; Pope, F.; Ngene, S.; Norton-Griffiths, M.; Frederick, H.; Okita-Ouma, B.; Douglas-Hamilton, I. Comparing an Automated High-Definition Oblique Camera System to Rear-Seat-Observers in a Wildlife Survey in Tsavo, Kenya: Taking Multi-Species Aerial Counts to the next Level. Biol. Conserv. 2020, 241, 108243.
  43. Pei, C.; She, Y.; Loewen, M. Deep Learning Based River Surface Ice Quantification Using a Distant and Oblique-Viewed Public Camera. Cold Reg. Sci. Technol. 2023, 206, 103736.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Imaging Science & Photographic Technology
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Styliani Verykokou
,
Charalabos Ioannidis
View Times: 877
Online Date: 02 Feb 2024
Table of Contents
    1000/1000
    Hot Most Recent
    Video Production Service
    Feedback