Geomatics and Environmental Engineering, vol. 17, no. 3

Issue

Vol. 17 No. 3 (2023): Geomatics and Environmental Engineering

Date Log

Submitted
Nov 8, 2022
Accepted
Jan 13, 2023
Published
Feb 1, 2023
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Optimising UAV Data Acquisition and Processing for Photogrammetry: A Review

https://doi.org/10.7494/geom.2023.17.3.29
Karolina Pargieła
AGH University of Science and Technology, Faculty of Geo-Data Science, Geodesy, and Environmental Engineering, Department of Photogrammetry, Remote Sensing of Environment, and Spatial Engineering, Krakow, Poland
https://orcid.org/0000-0002-5373-1240

Corresponding Author(s) : Karolina Pargieła

pargiela@agh.edu.pl

Geomatics and Environmental Engineering, Vol. 17 No. 3 (2023): Geomatics and Environmental Engineering

Abstract

Unmanned aerial vehicles (UAVs) are used to acquire measurement data for an increasing number of applications. Photogrammetric studies based on UAV data, thanks to the significant development of computer vision techniques, photogrammetry, and equipment miniaturization, allow sufficient accuracy for many engineering and non-engineering applications to be achieved. In addition to accuracy, development time and cost of data acquisition and processing are also important issues. The aim of this paper is to present potential limitations in the use of UAVs to acquire measurement data and to present measurement and processing techniques affecting the optimisation of work both in terms of accuracy and economy. Issues related to the type of drones used (multi-rotor, fixed-wing), type of shutter in the camera (rolling shutter, global shutter ), camera calibration method (pre-calibration, self-calibration), georeferencing method (direct, indirect), technique of measuring the external images orientation parameters (RTK, PPK, PPP), flight design methods and the type of software used were analysed.

Keywords

photogrammetry UAV optimising bundle adjustment tructure from motion
Pargieła, K. (2023). Optimising UAV Data Acquisition and Processing for Photogrammetry: A Review. Geomatics and Environmental Engineering, 17(3), 29–59. https://doi.org/10.7494/geom.2023.17.3.29
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. Park S., Choi Y.: Applications of unmanned aerial vehicles in mining from exploration to reclamation: A review. Minerals, vol. 10(8), 2020, 663. https://doi.org/10.3390/min10080663.
  2. Ren H., Zhao Y., Xiao W., Hu Z.: A review of UAV monitoring in mining areas: current status and future perspectives. International Journal of Coal Science & Technology, vol. 6, 2019, pp. 320–333. https://doi.org/10.1007/s40789-019-00264-5.
  3. Shahmoradi J., Talebi E., Roghanchi P., Hassanalian M.A.: A comprehensive review of applications of drone technology in the mining industry. Drones, vol. 4(3), 2020, 34. https://doi.org/10.3390/drones4030034.
  4. Barrile V., Candela G., Fotia A., Bernardo E.: UAV Survey of Bridges and Viaduct: Workflow and Application. [in:] Misra S., Gervasi O., Murgante B. et al. (eds.), Computational Science and Its Applications – ICCSA 2019: 19th International Conference, Saint Petersburg, Russia, July 1–4, 2019, Proceedings, Part IV, Lecture Notes in Computer Science, vol. 11622, Springer, Cham 2019, pp. 269–284. https://doi.org/10.1007/978-3-030-24305-0_21.
  5. Li Y., Liu Ch.: Applications of multirotor drone technologies in construction management. International Journal of Construction Management, vol. 19(5), 2019, pp. 401–412. https://doi.org/10.1080/15623599.2018.1452101.
  6. Zulkipli M.A., Tahar K.N.: Multirotor UAV-based photogrammetric mapping for road design. International Journal of Optics, vol. 2018, 2018, 1871058. https://doi.org/10.1155/2018/1871058.
  7. Dash J.P., Watt M.S., Pearse G.D., Heaphy M., Dungey H.S.: Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 131, 2017, pp. 1–14. https://doi.org/10.1016/j.isprsjprs.2017.07.007.
  8. Onishi M., Ise T.: Explainable identification and mapping of trees using UAV RGB image and deep learning. Scientific Reports, vol. 11, 2021, 903. https://doi.org/10.1038/s41598-020-79653-9.
  9. Torresan C., Berton A., Carotenuto F., Di Gennaro S.F., Gioli B., Matese A., Miglietta F. et al.: Forestry applications of UAVs in Europe: a review. International Journal of Remote Sensing, vol. 38(8–10), 2017, pp. 2427–2447. https://doi.org/10.1080/01431161.2016.1252477.
  10. Kerle N., Nex F., Gerke M., Duarte D., Vetrivel A.: UAV-based structural damage mapping: A review. ISPRS International Journal of Geo-Information, vol. 9(1), 2019, 14. https://doi.org/10.3390/ijgi9010014.
  11. Zwęgliński T.: The use of drones in disaster aerial needs reconnaissance and damage assessment – Three-dimensional modeling and orthophoto map study. Sustainability, vol. 12(15), 2020, 6080. https://doi.org/10.3390/su12156080.
  12. Barba S., Barbarella M., Di Benedetto A., Fiani M., Gujski L., Limongiello M.: Accuracy assessment of 3D photogrammetric models from an unmanned aerial vehicle. Drones, vol. 3(4), 2019, 79. https://doi.org/10.3390/drones3040079.
  13. Campana S.: Drones in archaeology. State-of-the-art and future perspectives. Archaeological Prospection, vol. 24(4), 2017, pp. 275–296. https://doi.org/10.1002/arp.1569.
  14. Chio S.-H., Chiang Ch.-Ch.: Feasibility study using UAV aerial photogrammetry for a boundary verification survey of a digitalized cadastral area in an urban city of Taiwan. Remote Sensing, vol. 12(10), 2020, 1682. https://doi.org/10.3390/rs12101682.
  15. Puniach E., Bieda A., Ćwiakąła P., Kwartnik-Pruc A., Parzych P.: Use of unmanned aerial vehicles (UAVs) for updating farmland cadastral data in areas subject to landslides. ISPRS International Journal of Geo-Information, vol. 7(8), 2018, 331. https://doi.org/10.3390/ijgi7080331.
  16. Yao H., Qin R., Chen X.: Unmanned aerial vehicle for remote sensing applications – A review. Remote Sensing, vol. 11(12), 2019, 1443. https://doi.org/10.3390/rs11121443.
  17. Colomina I., Molina P.: Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 92, 2014, pp. 79–97. https://doi.org/10.1016/j.isprsjprs.2014.02.013.
  18. Gerke M.: Developments in UAV-photogrammetry. Journal of Digital Landscape Architecture, vol. 3, 2018, pp. 262–272. https://doi.org/10.14627/537642028.
  19. González-Jorge H., Martínez-Sánchez J., Bueno M., Arias A.P.: Unmanned aerial systems for civil applications: A review. Drones, vol. 1(1), 2017, 2. https://doi.org/10.3390/drones1010002.
  20. Nikolakopoulos K., Soura K., Koukouvelas I., Argyropoulos N.: UAV vs classical aerial photogrammetry for archaeological studies. Journal of Archaeological Science: Reports, vol. 14, 2017, pp. 758–773. https://doi.org/10.1016/j.jasrep.2016.09.004.
  21. Gao M., Hugenholtz C.H., Fox T.A., Kucharczyk M., Barchyn T.E., Nesbit P.R.: Weather constraints on global drone flyability. Scientific Reports, vol. 11, 2021, 12092. https://doi.org/10.1038/s41598-021-91325-w.
  22. Roseman C.A., Argrow B.M.: Weather hazard risk quantification for sUAS safety risk management. Journal of Atmospheric and Oceanic Technology, vol. 37(7), 2020, pp. 1251–1268. https://doi.org/10.1175/JTECH-D-20-0009.1.
  23. Boon M.A., Drijfhout A.P., Tesfamichael S.: Comparison of a fixed-wing and multi-rotor UAV for environmental mapping applications: A case study. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-2/W6, 2017, pp. 47–54. https://doi.org/10.5194/isprs-archives-XLII-2-W6-47-2017.
  24. Boukoberine M.N., Zhou Z., Benbouzid M.: A critical review on unmanned aerial vehicles power supply and energy management: Solutions, strategies, and prospects. Applied Energy, vol. 255, 2019, 113823. https://doi.org/10.1016/j.apenergy.2019.113823.
  25. Battulwar R., Winkelmaier G., Valencia J., Naghadehi M.Z., Peik B., Abbasi B., Parvin B., Sattarvand J.: A practical methodology for generating high-resolution 3D models of open-pit slopes using UAVs: Flight path planning and optimization. Remote Sensing, vol. 12(14), 2020, 2283. https://doi.org/10.3390/rs12142283.
  26. Sanz-Ablanedo E., Chandler J.H., Rodríguez-Pérez J.R., Ordóñez C.: Accuracy of unmanned aerial vehicle (UAV) and SfM photogrammetry survey as a function of the number and location of ground control points used. Remote Sensing, vol. 10(10), 2018, 1606. https://doi.org/10.3390/rs10101606.
  27. Pargieła K., Rzonca A.: Determining optimal photogrammetric adjustment of images obtained from a fixed-wing UAV. The Photogrammetric Record, vol. 36(175), 2021, pp. 285–302. https://doi.org/10.1111/phor.12377.
  28. Wiącek P.: The database for multifactorial UAV accuracy assessments. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLIII-B5-2020, 2020, pp. 163–172. https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-163-2020.
  29. Grayson B., Penna N.T., Mills J.P., Grant D.S.: GPS precise point positioning for UAV photogrammetry. The Photogrammetric Record, vol. 33(164), 2018, pp. 427–447. https://doi.org/10.1111/phor.12259.
  30. Zhang H., Aldana-Jague E., Clapuyt F., Wilken F., Vanacker V., Van Oost K.: Evaluating the potential of PPK direct georeferencing for UAV-SfM photogrammetry and precise topographic mapping. Earth Surface Dynamics Discussions, 2019. https://doi.org/10.5194/esurf-2019-2.
  31. Chiabrando F., Lingua A., Maschio P., Teppati Losè L.: The influence of flight planning and camera orientation in UAVs photogrammetry. A test in the area of Rocca San Silvestro (LI), Tuscany. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-2/W3, 2017, pp. 163–170. https://doi.org/10.5194/isprs-archives-XLII-2-W3-163-2017.
  32. Kozmus Trajkovski K., Grigillo D., Petrovič D.: Optimization of UAV flight missions in steep terrain. Remote Sensing, vol. 12(8), 2020, 1293. https://doi.org/10.3390/rs12081293.
  33. Martínez-Carricondo P., Agüera-Vega F., Carvajal-Ramírez F., Mesas-Carrascosa F., García-Ferrer A., Pérez-Porras F.: Assessment of UAV-photogrammetric mapping accuracy based on variation of ground control points. International Journal of Applied Earth Observation and Geoinformation, vol. 10, 2018, pp. 1–10. https://doi.org/10.1016/j.jag.2018.05.015.
  34. Brach M., Chan J.Ch.-W., Szymanski P.: Accuracy assessment of different photogrammetric software for processing data from low-cost UAV platforms in forest conditions. iForest – Biogeosciences and Forestry, vol. 12(5), 2019, pp. 435–441. https://doi.org/10.3832/ifor2986-012.
  35. Jiang S., Jiang Ch., Jiang W.: Efficient structure from motion for large-scale UAV images: A review and a comparison of SfM tools. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 167, 2020, pp. 230–251. https://doi.org/10.1016/j.isprsjprs.2020.04.016.
  36. Clapuyt F., Vanacker V., Van Oost K.: Reproducibility of UAV-based earth topography reconstructions based on Structure-from-Motion algorithms. Geomorphology, vol. 260, 2016, pp. 4–15. https://doi.org/10.1016/j.geomorph.2015.05.011.
  37. Laporte-Fauret Q., Marieu V., Castelle B., Michalet R., Bujan S., Rosebery D.: Low-cost UAV for high-resolution and large-scale coastal dune change monitoring using photogrammetry. Journal of Marine Science and Engineering, vol. 7(3), 2019, 63. https://doi.org/10.3390/jmse7030063.
  38. Ludwig M., Runge C., Friess N., Koch T.L., Richter S., Seyfried S., Wraase L. et al.: Quality assessment of photogrammetric methods – A workflow for reproducible UAS orthomosaics. Remote Sensing, vol. 12(22), 2020, 3831. https://doi.org/10.3390/rs12223831.
  39. Chaudhry M.H., Ahmad A., Gulzar Q.: A comparative study of modern UAV platform for topographic mapping. IOP Conference Series: Earth and Environmental Science, vol. 540(1), 2020, 012019. https://doi.org/10.1088/1755-1315/540/1/012019.
  40. Hassanalian M., Abdelkefi A.: Classifications, applications, and design challenges of drones: A review. Progress in Aerospace Sciences, vol. 91, 2017, pp. 99–131. https://doi.org/10.1016/j.paerosci.2017.04.003.
  41. Tahar K.N., Ahmad A.: An evaluation on fixed wing and multi-rotor UAV images using photogrammetric image processing. International Journal of Computer and Information Engineering, vol. 7(1), 2013, pp. 48–52. https://doi.org/10.5281/zenodo.1078074.
  42. Ferrer-González E., Agüera-Vega F., Carvajal-Ramírez F., Martínez-Carricondo P.: UAV photogrammetry accuracy assessment for corridor mapping based on the number and distribution of ground control points. Remote Sensing, vol. 12(15), 2020, 2447. https://doi.org/10.3390/rs12152447.
  43. Štroner M., Urban R., Seidl J., Reindl T., Brouček J.: Photogrammetry using UAV-mounted GNSS RTK: Georeferencing strategies without GCPs. Remote Sensing, vol. 13(7), 2021, 1336. https://doi.org/10.3390/rs13071336.
  44. Reshetyuk Y., Mårtensson S.G.: Generation of highly accurate digital elevation models with unmanned aerial vehicles. The Photogrammetric Record, vol. 31(154), 2016, pp. 143–165. https://doi.org/10.1111/phor.12143.
  45. Templin T., Popielarczyk D., Kosecki R.: Application of low-cost fixed-wing UAV for Inland lakes shoreline investigation. Pure and Applied Geophysics, vol. 175, 2018, pp. 3263–3283. https://doi.org/10.1007/s00024-017-1707-7.
  46. Wiącek P., Pyka K.: The test field for UAV accuracy assessments. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-1/W2, 2019, pp. 67–73. https://doi.org/10.5194/isprs-archives-XLII-1-W2-67-2019.
  47. Benassi F., Dall’Asta E., Diotri F., Forlani G., Morra di Cella U., Roncella R., Santise M.: Testing accuracy and repeatability of UAV blocks oriented with GNSS-supported aerial triangulation. Remote Sensing, vol. 9(2), 2017, 172. https://doi.org/10.3390/rs9020172.
  48. Elkhrachy I.: Accuracy assessment of low-cost unmanned aerial vehicle (UAV) photogrammetry. Alexandria Engineering Journal, vol. 60(6), 2021, pp. 5579–5590. https://doi.org/10.1016/j.aej.2021.04.011.
  49. Zimmerman T., Jansen K., Miller J.: Analysis of UAS flight altitude and ground control point parameters on DEM accuracy along a complex, developed coastline. Remote Sensing, vol. 12(14), 2020, 2305. https://doi.org/10.3390/rs12142305.
  50. Ruggles S., Clark J., Franke K.W., Wolfe D., Reimschiissel B., Martin R.A., Okeson T.J., Hedengren J.D.: Comparison of SfM computer vision point clouds of a landslide derived from multiple small UAV platforms and sensors to a TLS-based model. Journal of Unmanned Vehicle Systems, vol. 4(4), 2016, pp. 246–265. https://doi.org/10.1139/juvs-2015-0043.
  51. Revuelto J., Alonso-Gonzalez E., Vidaller-Gayan I., Lacroix E., Izagirre E., Rodríguez-López G., López-Moreno J.I.: Intercomparison of UAV platforms for mapping snow depth distribution in complex alpine terrain. Cold Regions Science and Technology, vol. 190, 2021, 103344. https://doi.org/10.1016/j.coldregions.2021.103344.
  52. Kurczyński Z., Bielecki M.: Metric properties of rolling shutter low-altitude photography. Archiwum Fotogrametrii, Kartografii i Teledetekcji, vol. 29, 2017, pp. 177–190. https://doi.org/10.14681/afkit.2017.013.
  53. Vautherin J., Rutishauser S., Schneider-Zapp K., Choi H.F., Chovancova V., Glass A., Strecha Ch.: Photogrammetric accuracy and modeling of rolling shutter cameras. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. III-3, 2016, pp. 139–146. https://doi.org/10.5194/isprs-annals-III-3-139-2016.
  54. Zhou Y., Rupnik E., Meynard Ch., Thom Ch., Pierrot-Deseilligny M.: Simulation and analysis of photogrammetric UAV image blocks – Influence of camera calibration error. Remote Sensing, vol. 12(1), 2020, 22. https://doi.org/10.3390/rs12010022.
  55. Zhou Y., Daakir M., Rupnik E., Pierrot-Deseilligny M.: A two-step approach for the correction of rolling shutter distortion in UAV photogrammetry. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 160, 2020, pp. 51–66. https://doi.org/10.1016/j.isprsjprs.2019.11.020.
  56. İncekara A.H., Seker D.Z.: Rolling shutter effect on the accuracy of photogrammetric product produced by low-cost UAV. International Journal of Environment and Geoinformatics, vol. 8(4), 2021, pp. 549–553. https://doi.org/10.30897/ijegeo.948676.
  57. Griffiths D., Burningham H.: Comparison of preand self-calibrated camera calibration models for UAS-derived nadir imagery for a SfM application. Progress in Physical Geography: Earth and Environment, vol. 43(2), 2019, pp. 215–235. https://doi.org/10.1177/0309133318788964.
  58. Harwin S., Lucieer A., Osborn J.: The impact of the calibration method on the accuracy of point clouds derived using unmanned aerial vehicle multi-view stereopsis. Remote Sensing, vol. 7(9), 2015, pp. 11933–11953. https://doi.org/10.3390/rs70911933.
  59. Yusoff A.R., Mohd Ariff M.F., Idris K.M., Majid Z., Chong A.K.: Camera calibration accuracy at different UAV flying heights. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-2/W3, 2017, pp. 595–600. https://doi.org/10.5194/isprs-archives-XLII-2-W3-595-2017.
  60. Roncella R., Forlani G.: UAV block geometry design and camera calibration: A simulation study. Sensors, vol. 21(18), 2021, 6090. https://doi.org/10.3390/s21186090.
  61. Gerke M., Przybilla H.-J.: Accuracy analysis of photogrammetric UAV image blocks: Influence of onboard RTK-GNSS and cross flight patterns. Photogrammetrie – Fernerkundung – Geoinformation Jahrgang, Heft 1, 2016, pp. 17–30. https://doi.org/10.1127/pfg/2016/0284.
  62. Harwin S., Lucieer A.: Assessing the accuracy of georeferenced point clouds produced via multi-view stereopsis from unmanned aerial vehicle (UAV) imagery. Remote Sensing, vol. 4(6), 2012, pp. 1573–1599. https://doi.org/10.3390/rs4061573.
  63. James M.R., Robson S.: Mitigating systematic error in topographic models derived from UAV and ground-based image networks. Earth Surface Processes and Landforms, vol. 39(10), 2014, pp. 1413–1420. https://doi.org/10.1002/esp.3609.
  64. Cramer M., Przybilla H.-J., Zurhorst A.: UAV cameras: overview and geometric calibration benchmark. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-2/W6, 2017, pp. 85–92. https://doi.org/10.5194/isprs-archives-XLII-2-W6-85-2017.
  65. Radford C.R., Bevan G.: A calibration workflow for “prosumer” UAV cameras. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-2/W13, 2019, pp. 553–558. https://doi.org/10.5194/isprs-archives-XLII-2-W13-553-2019.
  66. Oniga V.-E., Pfeifer N., Loghin A.-M.: 3D calibration test-field for digital cameras mounted on unmanned aerial systems (UAS). Remote Sensing, vol. 10(12), 2018, 2017. https://doi.org/10.3390/rs10122017.
  67. Kılınç Kazar G., Karabörk H., Makineci H.B.: Evaluation of test field-based calibration and self-calibration models of UAV integrated compact cameras. Journal of the Indian Society of Remote Sensing, vol. 50, 2022, pp. 13–23. https://doi.org/10.1007/s12524-021-01454-y.
  68. Huang W., Jiang S., Jiang W.: Camera self-calibration with GNSS constrained bundle adjustment for weakly structured long corridor UAV images. Remote Sensing, vol. 13, 2021, 4222. https://doi.org/10.3390/rs13214222.
  69. Tournadre V., Pierrot-Deseilligny M., Faure P.H.: UAV linear photogrammetry. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XL-3/W3, 2015, pp. 327–333. https://doi.org/10.5194/isprsarchives-XL-3-W3-327-2015.
  70. Cledat E., Cucci D.A., Skaloud J.: Camera calibration models and methods for corridor mapping with UAVS. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. V-1-2020, 2020, pp. 231–238. https://doi.org/10.5194/isprs-annals-V-1-2020-231-2020.
  71. He F., Zhou T., Xiong W., Hasheminnasab S.M., Habib A.: Automated aerial triangulation for UAV-based mapping. Remote Sensing, vol. 10(12), 2018, 1952. https://doi.org/10.3390/rs10121952.
  72. Agüera-Vega F., Carvajal-Ramírez F., Martínez-Carricondo P.: Assessment of photogrammetric mapping accuracy based on variation ground control points number using unmanned aerial vehicle. Measurement, vol. 98, 2017, pp. 221–227. https://doi.org/10.1016/j.measurement.2016.12.002.
  73. James M.R., Robson S., d’Oleire-Oltmanns S., Niethammer U.: Optimising UAV topographic surveys processed with structure-from-motion: Ground control quality, quantity and bundle adjustment. Geomorphology, vol. 280, 2017, pp. 51–66. https://doi.org/10.1016/j.geomorph.2016.11.021.
  74. Ulvi A.: The effect of the distribution and numbers of ground control points on the precision of producing orthophoto maps with an unmanned aerial vehicle. Journal of Asian Architecture and Building Engineering, vol. 20(6), 2021, pp. 806–817. https://doi.org/10.1080/13467581.2021.1973479.
  75. Carvajal-Ramírez F., Agüera-Vega F., Martínez-Carricondo P.J.: Effects of image orientation and ground control points distribution on unmanned aerial vehicle photogrammetry projects on a road cut slope. Journal of Applied Remote Sensing, vol. 10(3), 2016, 034004. https://doi.org/10.1117/1.JRS.10.034004.
  76. Hilal A.H., Jasim O.Z., Ismael H.S.: Determination of the optimum number and distribution of the ground control points in stereo imaging to achieve precise positions. Journal of Physics: Conference Series, vol. 1973, 2021, 012191. https://doi.org/10.1088/1742-6596/1973/1/012191.
  77. Tomaštík J., Mokroš M., Surový P., Grznárová A., Merganič J.: UAV RTK/ PPK method – An optimal solution for mapping inaccessible forested areas? Remote Sensing, vol. 11(6), 2019, 721. https://doi.org/10.3390/rs11060721.
  78. Chudley T.R., Christoffersen P., Doyle S.H., Abellan A., Snooke N.: Highaccuracy UAV photogrammetry of ice sheet dynamics with no ground control. The Cryosphere, vol. 13(3), 2019, pp. 955–968. https://doi.org/10.5194/tc-13-955-2019.
  79. Stott E., Williams R.D., Hoey T.B.: Ground control point distribution for accurate kilometre-scale topographic mapping using an RTK-GNSS unmanned aerial vehicle and SfM photogrammetry. Drones, vol. 4(3), 2020, 55. https://doi.org/10.3390/drones4030055.
  80. Ekaso D., Nex F., Kerle N.: Accuracy assessment of real-time kinematics (RTK) measurements on unmanned aerial vehicles (UAV) for direct geo-referencing. Geo-spatial Information Science, vol. 23(2), 2020, pp. 165–181. https://doi.org/10.1080/10095020.2019.1710437.
  81. James M.R., Robson S., Smith M.W.: 3-D uncertainty-based topographic change detection with structure-from-motion photogrammetry: precision maps for ground control and directly georeferenced surveys. Earth Surface Processes and Landforms, vol. 42(12), 2017, pp. 1769–1788. https://doi.org/10.1002/esp.4125.
  82. Turner D., Lucieer A., Wallace L.: Direct georeferencing of ultrahigh-resolution UAV imagery. IEEE Transactions on Geoscience and Remote Sensing, vol. 52(5), 2014, pp. 2738–2745. https://doi.org/10.1109/TGRS.2013.2265295.
  83. Schaefer M., Teeuw R., Day S., Zekkos D., Weber P., Meredith T., van Westen C.J.: Low-cost UAV surveys of hurricane damage in Dominica: automated processing with co-registration of pre-hurricane imagery for change analysis. Natural Hazards, vol. 101, 2020, pp. 755–784. https://doi.org/10.1007/s11069-020-03893-1.
  84. Cucci D.A., Rehak M., Skaloud J.: Bundle adjustment with raw inertial observations in UAV applications. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 130, 2017, pp. 1–12. https://doi.org/10.1016/j.isprsjprs.2017.05.008.
  85. Eling Ch., Klingbeil L., Kuhlmann H.: Real-time single-frequency GPS/MEMS-IMU attitude determination of lightweight UAVs. Sensors, vol. 15(10), 2015, pp. 26212–26235. https://doi.org/10.3390/s151026212.
  86. Hugenholtz Ch., Brown O., Walker J., Barchyn T., Nesbit P., Kucharczyk M., Myshak S.: Spatial accuracy of UAV-derived orthoimagery and topography: Comparing photogrammetric models processed with direct geo-referencing and ground control points. Geomatica, vol. 70(1), 2016, pp. 21–30. https://doi.org/10.5623/cig2016-102.
  87. Padró J.-C., Muñoz F.-J., Planas J., Pons X.: Comparison of four UAV georeferencing methods for environmental monitoring purposes focusing on the combined use with airborne and satellite remote sensing platforms. International Journal of Applied Earth Observation and Geoinformation, vol. 75, 2019, pp. 130–140. https://doi.org/10.1016/j.jag.2018.10.018.
  88. Teppati Losè L., Chiabrando F., Giulio Tonolo F.: Boosting the timeliness of UAV large scale mapping. Direct georeferencing approaches: Operational strategies and best practices. ISPRS International Journal of Geo-Information, vol. 9(10), 2020, 578. https://doi.org/10.3390/ijgi9100578.
  89. Cledat E., Jospin L.V., Cucci D.A., Skaloud J.: Mapping quality prediction for RTK/PPK-equipped micro-drones operating in complex natural environment. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 167, 2020, pp. 24–38. https://doi.org/10.1016/j.isprsjprs.2020.05.015.
  90. Žabota B., Kobal M.: Accuracy assessment of UAV-photogrammetric-derived products using PPK and GCPs in challenging terrains: In search of optimized rockfall mapping. Remote Sensing, vol. 13(19), 2021, 3812. https://doi.org/10.3390/rs13193812.
  91. Famiglietti N.A., Cecere G., Grasso C., Memmolo A., Vicari A.: A test on the potential of a low cost unmanned aerial vehicle RTK/PPK solution for precision positioning. Sensors, vol. 21(11), 2021, 3882. https://doi.org/10.3390/s21113882.
  92. Zumberge J.F., Heflin M.B., Jefferson D.C., Watkins M.M., Webb F.H.: Precise point positioning for the efficient and robust analysis of GPS data from large networks. Journal of Geophysical Research: Solid Earth, vol. 102(B3), 1997, pp. 5005–5017. https://doi.org/10.1029/96JB03860.
  93. Gross J.N., Watson R.M., D’Urso S., Gu Y.: Flight-test evaluation of kinematic precise point positioning of small UAVs. International Journal of Aerospace Engineering, vol. 2016, 2016, 1259893. https://doi.org/10.1155/2016/1259893.
  94. 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, vol. 38(4), 2013, pp. 421–430. https://doi.org/10.1002/esp.3366.
  95. Gómez-López J.M., Pérez-García J.L., Mozas-Calvache A.T., DelgadoGarcía J.: Mission flight planning of RPAS for photogrammetric studies in complex scenes. ISPRS International Journal of Geo-Information, vol. 9(6), 2020, 392. https://doi.org/10.3390/ijgi9060392.
  96. Torres-Sánchez J., López-Granados F., Borra-Serrano I., Peña J.M.: Assessing UAV-collected image overlap influence on computation time and digital surface model accuracy in olive orchards. Precision Agriculture, vol. 19, 2018, pp. 115–133. https://doi.org/10.1007/s11119-017-9502-0.
  97. Gandor F., Rehak M., Skaloud J.: Photogrammetric mission planner for RPAS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XL-1/W4, 2015, pp. 61–65. https://doi.org/10.5194/isprsarchives-XL-1-W4-61-2015.
  98. Pepe M., Fregonese L., Scaioni M.: Planning airborne photogrammetry and remote-sensing missions with modern platforms and sensors. European Journal of Remote Sensing, vol. 51(1), 2018, pp. 412–435. https://doi.org/10.1080/22797254.2018.1444945.
  99. Mesas-Carrascosa F.-J., Notario García M.D., Meroño de Larriva J.E., García-Ferrer A.: An analysis of the influence of flight parameters in the generation of unmanned aerial vehicle (UAV) orthomosaicks to survey archaeological areas. Sensors, vol. 16(11), 2016, 1836. https://doi.org/10.3390/s16111838.
  100. Abou Chakra C., Somma J., Gascoin S., Fanise P., Drapeau L.: Impact of flight altitude on unmanned aerial photogrammetric survey of the snow height on Mount Lebanon. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLIII-B2-2020, 2020, pp. 119–125. https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-119-2020.
  101. Quoc Long N., Goyal R., Khac Luyen B., Van Canh L., Xuan Cuong C., Van Chung P., Ngoc Quy B., Bui X.N.: Influence of flight height on the accuracy of UAV derived digital elevation model of complex terrain. Inżynieria Mineralna, t. 1, nr 1(45), 2020, pp. 179–187. https://doi.org/10.29227/IM-2020-01-27.
  102. Elhadary A., Rabah M., Ghanim E., Mohie R., Taha A.: The influence of flight height and overlap on UAV imagery over featureless surfaces and constructing formulas predicting the geometrical accuracy. NRIAG Journal of Astronomy and Geophysics, vol. 11(1), 2022, pp. 210–223. https://doi.org/10.1080/20909977.2022.2057148.
  103. Manconi A., Ziegler M., Blöchliger T., Wolter A.: Technical note: optimization of unmanned aerial vehicles flight planning in steep terrains. International Journal of Remote Sensing, vol. 40(7), 2019, pp. 2483–2492. https://doi.org/10.1080/01431161.2019.1573334.
  104. Alidoost F., Arefi H.: Comparison of UAS-based photogrammetry software for 3D point cloud generation: A survey over a historical site. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. IV-4/W4, 2017, pp. 55–61. https://doi.org/10.5194/isprs-annals-IV-4-W4-55-2017.
  105. Casella V., Chiabrando F., Franzini M., Manzino A.M.: Accuracy assessment of a UAV block by different software packages, processing schemes and validation strategies. ISPRS International Journal of Geo-Information, vol. 9, 2020, 164. https://doi.org/10.3390/ijgi9030164.
  106. Iglhaut J., Cabo C., Puliti S., Piermattei L., O’Connor J., Rosette J.: Structure from motion photogrammetry in forestry: A review. Current Forestry Reports, vol. 5, 2019, pp. 155–168. https://doi.org/10.1007/s40725-019-00094-3.
  107. Jaud M., Passot S., Le Bivic R., Delacourt C., Grandjean P., Le Dantec N.: Assessing the accuracy of high resolution digital surface models computed by PhotoScan® and MicMac® in sub-optimal survey conditions. Remote Sensing, vol. 8(6), 2016, 465. https://doi.org/10.3390/rs8060465.
  108. Przybilla H.J., Gerke M., Dikhoff I., Ghassoun Y.: Investigations on the geometric quality of cameras for UAV applications using the high precision UAV test field Zollern colliery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-2/W13, 2019, pp. 531–538. https://doi.org/10.5194/isprs-archives-XLII-2-W13-531-2019.
  109. Govorčin M., Pribičević B., Ðapo A.: Comparison and analysis of software solutions for creation of a digital terrain model using unmanned aerial vehicles. [in:] 14th International Multidisciplinary Scientific GeoConference SGEM 2014: Conference Proceedings, Sofia, 2014. https://doi.org/10.13140/2.1.2352.4803.
  110. Pell T., Li J.Y.Q., Joyce K.E.: Demystifying the differences between structure-from-motion software packages for pre-processing drone data. Drones, vol. 6(1), 2022, 24. https://doi.org/10.3390/drones6010024.
  111. Sharma M., Raghavendra S., Agrawal S.: Development of an open-source tool for UAV photogrammetric data processing. Journal of the Indian Society of Remote Sensing, vol. 49(3), 2021, pp. 659–664. https://doi.org/10.1007/s12524-020-01237-x.
Read More
Author biographies is not available.
Download this PDF file
PDF
Statistic
Read Counter : 743 Download : 626

Downloads

Download data is not yet available.