Oblique Aerial Images: Geometric Principles, Relationships and Definitions: History
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Contributor:
Styliani Verykokou
,
Charalabos Ioannidis

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.

This entry is adapted from the peer-reviewed paper 10.3390/encyclopedia4010019

References

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  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.
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