Effects of Density of Ground Control Points On the Accuracy of Orthophotos Produced Using UAV

References

[1] Ahmed, A., and Udin, W. (2014). Assessment of photogrammetric mapping accuracy based on variation flying altitude using unmanned aerial vehicles. Geographical Information Science and Remote Sensing, 48(1), 50-73.

[2] Balletti, C., Guerra, F., Tsioukas, V., and Vernier, P. (2014). Calibration of action cameras for photogrammetric purposes. Sensors, 14(9), 17471-17490.

[3] Bryson, M., and Sukkarieh, (2009). Architectures for cooperative airbone simultaneous localization and mapping. Journal of Intelligent and Robotic System, Special Issues on Airbone SLAM, 55(4), 267-297.

[4] Bupe, P., Haddad, R., and Rios-Gutierrez, F. (2015). Relief and emergency communication network based on an autonomous decentralized UAV clustering network. SoutheastCon 1–8, doi:10.1109/SEC9ON.2015.7133027.

[5] Campbell, J. B., and Wynne, R. H. (2011). Introduction to Remote Sensing. Guilford Press.

[6] Cao, M., Bo, Z. and Li, Y. (2016). Rapid production of UAV imagery without control point data. Surveying and Mappinng, 8(1), 35–38.

[7] Cesetti, E., Frontoni, A., Mancini, A., Ascani, P., and Zingaretti, S. (2011). A visual global positioning system for unmanned aerial vehicles used in photogrammetric applications. Journal of Intelligent Robot System, 61(1), 157-168.

[8] Clapuyt, F., Vanacker, V. and Van Oost, K. (2016). Reproducibility of UAV-based earth topography reconstructions based on structure from motion algorithms. Geomorphology, 260(1), 4-15.

[9] De Agostino, M., Manzino A.M, and Piras M. (2010). Performances comparison of different MEMS-based IMUs. Position, Location and Navigation Symposium, 187–201, doi:101109/PLANS.2010.5507128, date of conference, 4-6 May 2010.

[10] Francesco, N., and Fabio R., (2012). UAV photogrammetry for mapping and 3D modelling current status and future perspectives. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 38(1), 26-27.

[11] Gatewing (2013). Support Bulletin Image Overlap Settings for Gatewing X100 and Trimble UX5 Aerial Imaging Solutions. Available at: http://surveypartners.trimble.com (Accessed: 12 August 2018).

[12] Hayat, S., Yanmaz, E., and Muzaffar R. (2016). Survey on Unmanned Aerial Vehicle Networks for Civil Applications: A Communications Viewpoint. Communications Surveys & Tutorials, 18(4), 2624–2661.

[13] He J., Li Y. and Zhang K. (2012). Research of UAV flight planning paramaters. Postioning, 03(4), 43-45.

[14] Hervouet, R., Dunford, H., Piegay, B.,and Tremelo, M. (2011). Analysis of post-flood recruitment patterns in braided-channel Rivers at multiple scales based on an image series collected by unmanned aerial vehicles, ultra-light aerial vehicles and satellites. Geographical Information Science and Remote Sensing, 48(1), 50-73.

[15] Howell, E. (2018). Using high resolution UAV thermal imagery to assess the variability in the water status of five fruit tree species within a commercial orchard. Precision Agriculture, 14(6), 660–678.

[16] Jaakkola, J., Hyppa, A., Kukko, X., Kaartinen, M., and Lehtomaki, Y. (2010). A low-cost multi-sensoral mobile mapping system and its feasibility for tree measurements. Journal of Photogrammetry and Remote Sensing, 65(1), 514-522.

[17] James, M., and Robson, S. (2012). Straightforward reconstruction of 3D surfaces and topography with a camera: Accuracy and geoscience application. Journal of Geophysics and Earth Surface, 117.

[18] Korchenko, A., and Illyash O. (2013). The generalized classification of unmanned air vehicles. Second International Conference on Actual Problems of Unmanned Air Vehicles Developments. Proceedings, 28–34.

[19] Leitao, J., Moy de Vitry, M., Scheidegger, A., and Rieckermann, J. (2016). Assessing the quality of digital elevation models obtained from mini unmanned aerial vehicles for overland flow modeling in urban areas. Hydrographic Earth Science, 20(1), 1637-1653.

[20] Li J., Gang A., Zhang Y. and Lucas P. (2018). A review on deep learning in UAV remote sensing. International Journal of Applied Earth Observation and Geoinformation, 102(1), 1024-1056.

[21] Li, J., Yuqiang, Z., and Vericat, D. (2017). From experimental plots to experimental landscapes: Topography, erosion and deposition in sub-humid badlands from Structure-from-Motion photogrammetry. Earth Surface, 40(1), 1656–1671.

[22] Liu Y., Xinqi, Z., Gang A., Zhang Y. and Yuqiang, Z. (2018). Generating a high-precision true digital orthophoto map based on UAV images. International Journal of Geographic Information, 7(1), 333.

[23] Mian, O., Lutes, J., Lipa, G., Hutton, J., Gavelle E. and Borghini, S. (2016). Accuracy assessment of direct georeferencing for photogrammetric applications on small unmanned aerial platforms. Remote Sensing and Spatial Information Science, 40(1), 77–83.

[24] Motlagh, H., Taleb, T. and Arouk, O. (2016). Low-altitude unmanned aerial vehicles-based internet of things services: Comprehensive survey and future perspectives. Internet of Things Journal, 3(6), 899–922.

[25] Neitzel, F., and Klonowski, J. (2011). Mobile 3D mapping with low-cost UAV system. Remote Sensing and Spatial Information Sciences, 38(1), 22.

[26] Nex F. and Remondino F. (2014). UAV for 3D mapping applications: a review. Applied Geomatics 6(1), 1-15.

[27] Piras, M., Marucco, G., and Charqane, K. (2010). Statistical analysis of different low-cost GPS receivers for indoor and outdoor positioning. Position Location and Navigation Symposium, (PLANS), June 2010, doi:10.1109/PLANS.2010.5507325, 838-849.

[28] Pix4D, UAV Mapping Software. Available online: https://pix4d.com (accessed on 23 May, 2021).

[29] Rock, G., Ries, J., and Udelhoven, T. (2011). Sensitively analysis of UAV photogrammetry for creating gigital elevation model (DEM). In proceeding for the International archives of photogrammetry remote sensing and spatial information science, Zurich, Switzerland

[30] Rosnell, T. and Honkavaara, E. (2011). Point cloud generation from aerial image data acquired by a quadrocopter type micro unmanned aerial vehicle and digital still camera. Sensor, 12(1), 453-480.

[31] Sabbane, Y. (2015). Smart autonomous aircraft flight control and planning for UAV (1st edition), CRC press, https://doi.org/10.1201/b19350.

[32] Saskia, G., Ruedi B. and Daniel F. (2017). Accuracy asssessmeng of digital surface model from unmanned aerial vehicles imagery from glacier. Remote Sensing, 9(2), 186.

[33] Shahbazi, M., Gunho S., Jerome, T., and Patrick, M. (2018). Development and evaluation of a UAV-photogrammetry system for precise 3D environmental modeling. Sensors, 15(1), 27493-27524.

[34] Sharan, K., Mohammed, A., Nur, S. and William, C. (2018). Method for the visualization of landform by mapping using low altitude UAV application. Materials Science and Engineering, 352(1), 345-347.

[35] Smith, M., Carrivick, J. and Quincey D. (2016). Structure from motion photogrammetry in physical geography. Physical Geography, 12(3), 247–275.

[36] Sona, G., Pinto L., Pagliari D., Passoni D. and Gini, R. (2014). Experimental analysis of different software packages for orientation and digital surface modelling from UAV images. Earth Science Information, 7, 97–107.

[37] Stella, P., Jock S. and Olympia T. (2019). Unmanned aerial vehicle (UAV) based mapping in engineering surveys: Technical considerations for optimum results. Engineering geology, 232(1), 12-21.

[38] Tahar K. (2012). An evaluation on different number of ground control points in unmanned aerial vehicle photogrammetry block. International archives of photogrammetry remote sensing and spatial information science, 3(1), 10-15.

[39] Tahar, K. (2013). An Evaluation on Different Number of Ground Control Points in Unmanned Aerial Vehicle Photogrammetric Block. ISPRS International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, 40(1), 1656–1671.

@article{1712735,
      author = {Bello Isah Kontagora, Muhammad Bala, Jiya Jacob},
      title = {Effects of Density of Ground Control Points On the Accuracy of Orthophotos Produced Using UAV},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {6},
      pages = {1118-1126},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1712735.pdf},
      abstract = {Ground control points are needed in the production process of any accurate photogrammetric output. They are used to georeference the acquired images. The aim of this research is to investigate the effects of density and distribution of ground control points on the accuracy of orthophotos produced by UAVs. To achieve this, an area of 15.2 hectares was photographed with DJI Marvic Air UAV. One hundred and eighty (180) images were captured at an altitude of 60m above the ground. Ground control points (GCPs) and check points (CPs) were established using Hi-Target GNSS with external antenna. The images acquired were processed using Pix4Dmapper photogrammetric software. Bundle adjustment was performed with the number of GCPs increased from eight (8) to nineteen (19). The orthophoto generated were analyzed by comparing the ground coordinates with orthomosaic coordinates. Root mean square error (RMSE) analysis was conducted, one for each of the configuration of GCPs. The smaller the RMSE value, the higher the accuracy of the orthophoto. The configuration with 19 GCPs produced the best result with RMSE of 1.2195 m, ?0.8461 m and ?0.0129 m for easting, northing and height respectively. It can be concluded that the density and distribution of the GCPs have an effect on the production and accuracy of orthophotos. Difference in accuracy in respects to GSD?s should be tested out to obtain the optimal GSD for mapping and number of GCP?s for study area consists of differential elevation profile.},
      keywords = {Unmanned Aerial Vehicle; Agisoft Photoscan; Digital Elevation Model; Orthophoto},
      month = {December},
      doi = {https://doi.org/10.64388/IREV9I6-1712735}
  }