New criteria for optimizing the functioning of imaging devices installed on high-speed unmanned aerial vehicles


DOI: 10.34759/trd-2022-122-20

Аuthors

Zulfugarli P. R.

Azerbaijan Technical University, 25, Hussein Javid prosp., Baku, 370073, Azerbaijan

e-mail: Peri.rzayeva30@gmail.com

Abstract

Effective visual monitoring of the ground situation can be carried out using various imaging devices installed on the UAV. In this case, the spatial resolution can be up to 1 cm. The dominant direction here is the fulfillment of the criterion for overlapping images of each other, obtained at the rate of UAV movement. It is common knowledge. That in order to obtain a high-quality orthomosaic image, adjacent terrain images obtained during the UAV flight should overlap 60% in the forward direction and 20% in the direction perpendicular to the direction of movement. At the same time, when the carrier is flying at supersonic speed and the requirements for the overlap of time-sequential images are often not met. In such cases, qualitatively new criteria are required to optimize imaging systems. The resolution of photogrammetric images obtained from aircraft equipped with imaging devices is determined by such a metric as the Ground Sample Distance, or GSD for short, which is defined as the distance between two adjacent pixels.

With regard to high-speed flying objects equipped with imaging devices, the following optimization criteria have been proposed: (1) Design criterion α_1, (2) Functional optimization criterion α_2 The first criterion is put forward in order to achieve small design dimensions of the imaging unit for a given value of the number of pixels per unit image width and GSD = const. In turn, the second criterion is put forward from the conditions for achieving stealth devices for various detection systems. The solution of the formulated optimization problems showed that the criteria α_1 and α_2 are fulfilled with a square root dependence of the distance from the sensor to the ground object and the size of the sensor area on the focal length of the device, respectively.

Keywords:

UAV, imaging system, optimization, camera, criterion

References

  1. Deb S.K., El-Kadi A.I. Susceptibility assessment of shallow landslides on Oahu, Hawaii, under extreme-rainfall events, Geomorphology, 2009, no. 108 (3-4), pp. 219-233. DOI:10.1016/j.geomorph.2009.01.009

  2. Dehn M., Buma J. Modelling future landslide activity based on general circulation models, Geomorphology, 1999, no. 30, pp. 175-187. DOI:10.1016/S0169-555X(99)00053-7

  3. Hölbling D., Abad L., Dabiri, Z., Prasicek, G., Tsai, T.-T., Argentin A.-L. Mapping and Analyzing the Evolution of the Butangbunasi Landslide Using Landsat Time Series with Respect to Heavy Rainfall Events during Typhoons, Applied Sciences, 2020, no. 10 (2), pp. 630. DOI:10.3390/app10020630

  4. Chen X., Sun Q., Hu J. Generation of Complete SAR Geometric Distortion Maps Based on DEM and Neighbor Gradient Algorithm, Applied Sciences, 2018, no. 8 (11), pp. 2206. DOI:10.3390/app8112206

  5. Westoby M.J., Brasington J., Glasser N.F., Hambrey M.J., Reynolds J.M. Structure-from-Motion photogrammetry: A low-cost, effective tool for geoscience applications, Geomorphology, 2012, no. 179, pp. 300–314. DOI:10.1016/j.geomorph.2012.08.021

  6. Fonstad M.A., Dietrich J.T., Courville B.C., Jensen J.L., Carbonneau P.E. Topographic structure from motion: A new development in photogrammetric measurement, Earth Surface Processes and Landforms, 2013, no. 38, pp. 421–430. DOI:10.1002/esp.3366

  7. Li H., Chen L., Wang Z., Yu Z. Mapping of River Terraces with Low-Cost UAS Based Structure-from-Motion Photogrammetry in a Complex Terrain Setting, Remote Sensing, 2019, no. 11, pp. 464. DOI:10.3390/rs11040464

  8. Yang C.-J., Yeh L.-W., Cheng Y.-C., Jen C.-H., Lin J.-C. Badland Erosion and Its Morphometric Features in the Tropical Monsoon Area, Remote Sensing, 2019, no.11, pp. 3051. DOI:10.3390/rs11243051

  9. Jordan B.R. A birds-eye view of geology: The use of micro drones/UAVs in geologic filed work and education, Geological Society of America today, 2015, vol. 25 (7), pp. 42-43. DOI:10.1130/GSATG232GW.1

  10. Bemis S.P., Micklethwaite S., Turner D., James M.R., Akciz S., Thiele S.T., Bangash H.A. Ground – based and UAV- based photogrammetry: A multi-scale, high – resolution mapping tool for structural geology and paleoseismology, Journal of Structural Geology, 2014, vol. 68, pp. 163-178. DOI:10.1016/j.jsg.2014.10.007

  11. Pytharouli S., Souter J., Tziavou O. Unmanned aerial vehicle (UAV) based mapping in engineering surveys: Technical considerations for optimum results, 4th Joint International Symposium on Deformation Monitoring (JISDM), Athens, Greece, May 2019, vol. 15-17.

  12. Loyola-Jacob N., La Rivera-Munoz F., Herrera R.F., Atencio E. Unmanned aerial vehicles (UAVs) for physical progress monitoring of construction, Sensors, 2021, vol. 21 (12), pp. 4227. DOI:10.3390/s21124227

  13. Wierzbicki D. Multi-camera imaging system for UAV photogrammetry, Sensors, 2018, vol. 18, pp. 2433. DOI:10.3390/s18082433

  14. Aslanova A.B. Trudy MAI, 2021, no. 119. URL: http://trudymai.ru/eng/published.php?ID=159794. DOI: 10.34759/trd-2021-119-16

  15. Sentsov A.A., Nenashev V.A., Ivanov S.A., Turnetskaya E.L. Trudy MAI, 2021, no. 117. URL: http://trudymai.ru/eng/published.php?ID=156227. DOI: 10.34759/trd-2021-117-08

  16. Trusfus M.V., Abdullin I.N. Trudy MAI, 2021, no. 116. URL: http://trudymai.ru/eng/published.php?ID=121099. DOI: 10.34759/trd-2021-116-13

  17. Manuel de Luis-Ruiz J., Sedano-Cibrian J., Garcia-Pereda R., Perez-Alverez R., Malagon-Picon B. Optimization of photogrammetric flights with UAVs for the metric virtualization of archaeological sites. Application to Juliobriga (Cantabria, Spain), Applied Sciences, 2021, vol. 2, pp. 1204. DOI:10.3390/APP11031204

  18. Visockiene J. S., Puziene R., Stanionis A., Tumeliene E. Unmanned aerial vehicles for photogrammetry: analysis of orthophoto images over the Territory of Lithuania, International Journal of Aerospace Engineering, 2016, vol. 1, pp. 1-9. DOI: 10.1155/2016/4141037

  19. Mizoguchi N., Oku H., Ishikawa M. High – speed variable – focus optical system for extended depth of field, IEEE International Symposium on Industrial Electronics, Seul, Korea, July 5-8 2009, pp. 1668-1673. DOI:10.1109/ISIE.2009.5222715

  20. El'sgol'ts L.E. Differentsial'nye uravneniya i variatsionnoe ischislenie (Differential equations and calculus of variations), Moscow, Nauka, 1974, 432 p.


Download

mai.ru — informational site MAI

Copyright © 2000-2024 by MAI

 Вход