An assessment of the accuracy of structure-from-motion (SfM) photogrammetry for 3D terrain mapping

• Autorzy: Iheaturu Chima Jude , Ayodele Emmanuel Gbenga , Okolie Chukwuma John

Identyfikatory

DOI

10.15576/GLL/2020.2.65

• Języki publikacji: EN

Abstrakty

EN

Unmanned Aerial Vehicles (UAVs) equipped with photogrammetric or remote sensing instrumentations offer numerous opportunities in mapping and data collection for topographic modelling. An example is an emerging technique known as Structure-from-Motion (SfM) photogrammetry used for the collection of low-cost, high spatial resolution, three-dimensional data. This study utilised the real time kinematic-based point-to-point validation technique and two sets of randomly selected ground control points to assess the capability and geometric accuracy of SfM-technology for three-dimensional (3D) terrain mapping over a small study area to contribute to the knowledge of applicability. The data used was collected in Garscube Sports Complex, Glasgow City Council, Scotland. The study utilised fifteen (15) Ground Control Points (GCPs) coordinated by the Real Time Kinematic Global Navigation Satellite System (RTK GNSS) positioning technique, while a DJI Phantom 3 Professional unmanned aerial vehicle was used to obtain the aerial photos in a single flight to minimise cost. The processing of the photos was done using Pix4Dmapper Pro software version 4.2.27. A point-to-point validation method was used to evaluate the 3D positional accuracy of the orthophoto and DSM. The results of the validation with ten checkpoints suggest a high level of accuracy and acceptability given a Root Mean Square Error of 20.93 mm, 18.48 mm and 46.05 mm in the X, Y and Z coordinates respectively. In conclusion, the study has shown that SfM technique can be used to produce high-resolution and accurate topographic data for geospatial applications with significant advantages over the traditional methods. However, it is to be noted that the quality of the data captured is dependent on the methodology adopted and should be taken into consideration.

Słowa kluczowe

EN

SfM photogrammetry; orthophoto; DSM; topography; GNSS

PL

fotogrametria SfM; ortofotomapa; DSM; GNSS; topografia

• Wydawca: Wydawnictwo Uniwersytetu Rolniczego w Krakowie
• Czasopismo: Geomatics, Landmanagement and Landscape
• Rocznik: 2020
• Tom: no. 2
• Strony: 65--82
• Opis fizyczny: Bibliogr. 44 poz., rys, tab.

Twórcy

autor

Iheaturu Chima Jude

• Imo State University, Nigeria Department of Surveying and Geoinformatics

autor

Ayodele Emmanuel Gbenga

• University of Lagos, Nigeria Department of Surveying and Geoinformatics

autor

Okolie Chukwuma John

• University of Lagos, Nigeria Department of Surveying and Geoinformatics

Bibliografia

• Burns J.H.R., Delparte D., Gates R.D., Takabayashi M. 2015. Integrating structure-from-motion photogrammetry with geospatial software as a novel technique for quantifying 3D ecological characteristics of coral reefs. Peer J 3:e1077. DOI: 10.7717/peerj.1077
• Caroti G., Zaragoza I.M.E., Piemonte A. 2015. Accuracy assessment in structure from motion 3D reconstruction from UAV-born images: The influence of the data processing methods. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(1), 103–109.
• Chai T., Draxler R.R. 2014. Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7, 1247–1250. https://doi.org/10.5194/gmd-7-1247-2014.
• Dietrich J.T. 2014. Applications of Structure-from-Motion Photogrammetry to Fluvial Geomorphology. PhD Thesis, University of Oregon, USA.
• DJI 2019. DJI Phantom 3 Professional. https://www.dji.com/phantom-3-pro [accessed: 4.10.2019].
• Edwards E.J., Clarke P.J., Penna N.T., Goebell S. 2010. An examination of network RTK GPS services in Great Britain. Survey Review, 42(316), 107–121.
• Entwistle N.S., Heritage G. 2017. An evaluation DEM accuracy acquired using a small unmanned aerial vehicle across a riverine environment. International Journal of New Technology and Research, 3(17), 43–48.
• Furukawa Y., Ponce J. 2007. Accurate, dense, and robust multi-view stereopsis. In: Proceedings, IEEE Conference on Computer Vision and Pattern Recognition 2007, 1–8.
• Furukawa Y., Curless B., Seitz M., Szeliski R. 2010. Clustering view for multi-view stereo. In: Proceedings, IEEE Conference on Computer Vision and Pattern Recognition 2010, 1434–1441.
• Gerke M. 2009. Dense Matching in High Resolution Oblique Airborne Images. ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., XXXVIII, 77–82.
• Harwin S., Lucieer A. 2012. Assessing the accuracy of georeferenced point clouds produced via multi-view stereopsis from unmanned aerial vehicle (UAV) imagery. Remote Sensing, 4(6), 1573–1599. https://doi.org/10.3390/rs4061573
• Heindel R.C., Chipman J.W., Dietrich J.T., Virginia R.A. 2018. Quantifying rates of soil deflation with Structure-from-Motion photogrammetry in west Greenland. Arctic, Antarctic, and Alpine Research, 50(1), S100012, 14pps. doi: 10.1080/15230430.2017.1415852
• Ishida K. 2017. Investigating the accuracy of 3D models created using SfM. 34th International Symposium on Automation and Robotics in Construction, 834–839.
• Jordan J.H. 2017. Modeling Ozark Caves with Structure-from-Motion Photogrammetry: An Assessment of Stand-Alone Photogrammetry for 3-Dimensional Cave Survey. Theses and Dissertations, 2406 (2017). University of Arkansas. http://scholarworks.uark.edu/etd/2406.
• Liu Y., Zheng X., Ai G., Zhang Y., Zuo Y. 2018. Generating a High-Precision True Digital Orthophoto Map Based on UAV Images. In: ISPRS International Journal of Geoinformation, 7 (333), 15pps. doi:10.3390/ijgi7090333.
• Lowe D. 2004. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60, 91–110.
• Luhmann T., Robson S. 2006. Close Range Photogrammetry Principles, Techniques and Applications. Whittles, Dunbeath, UK.
• Micheletti N., Chandler J.H., Lane S.N. 2015. Structure from motion (SFM) photogrammetry. In: Geomorphological Techniques (online edition), eds. L.E. Clarke, J.M. Nield. British Society for Geomorphology, London, Chap. 2, Sec. 2.2.
• Mlambo R., Woodhouse I.H., Gerard F., Anderson K. 2017. Structure from Motion (SfM) Photogrammetry with Drone Data: A Low Cost Method for Monitoring Greenhouse Gas Emissions from Forests in Developing Countries. Forests, 8(68), 20pps. doi:10.3390/f8030068.
• Nesbit P., Hugenholtz C. 2019. Enhancing UAV–SfM 3D Model Accuracy in High-Relief Landscapes by Incorporating Oblique Images. Remote Sensing, 11(3), 239. MDPI AG. http://dx.doi.org/10.3390/rs11030239.
• Nex F., Gerke M., Remondino F., Przybilla H.-J., Bäumker M., Zurhorst A. 2015. Benchmark for Multi-Platform Photogrammetry. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci., II-3/W4,135–142.
• Nilosek D., Walvoord D.J., Salvaggio C. 2014. Assessing geoaccuracy of structure from motion point clouds from long range image collections. Optical Engineering, 53(11), 113112. doi: 10.1117/1.OE.53.11.113112
• Nouwakpo S.K., Weltz M.A., Mcgwire K. 2015. Assessing the performance of structure-from-motion photogrammetry and terrestrial LiDAR for reconstructing soil surface microtopography of naturally vegetated plots. Earth Surface Processes and Landforms, 41(3), 308–322. doi: 10.1002/esp.3787.
• Onwudinjo K.C., Smit J.L. 2019. Evaluating the Performance of Multi-Rotor Unmanned Aerial Vehicle – Structure from Motion (UAV-SfM) Imagery in Assessing Simple and Complex Forest Structures: Comparison to Airborne and Terrestrial Laser Scanning. 6th EBE Research Expo, University of Cape Town. doi: 10.13140/RG.2.2.24115.48164
• Ostwald A.M., Hurtado J.M. 2017. 3D Models from Structure-from-Motion Photogrammetry using Mars Science Laboratory Images: Methods and Implications. Lunar and Planetary Science, XLVIII (2017). 2pps.
• Ostrowski W. 2016. Accuracy of measurements in oblique aerial images for urban environment. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch., 42, 79–85.
• Panagiotidis D., Surový P., Kuželka K. 2016. Accuracy of Structure from Motion models in comparison with terrestrial laser scanner for the analysis of DBH and height influence on error behaviour. Journal of Forest Science, 62(8), 357–365. doi: 10.17221/92/2015-JFS
• Pezzuolo A., Giora D., Sartori L., Guercini S. 2018. Automated 3D Reconstruction of Rural Buildings from Structure-from-Motion (SfM) Photogrammetry Approach, 23–25. doi: 10.22616/ERDev2018.17. N060
• PIX4D Support. 2017a. Ground Sampling Distance, GSD. https://support.pix4d.com/hc/en-us/articles/202559809-Ground-Sampling-Distance-GSD-#gsc.tab=0. (2017a) [accessed: 30.11.2017].
• PIX4D Support. 2017b. Using GCPs. https://support.pix4d.com/hc/en-us/articles/202558699-Using-GCPs#gsc.tab=0. (2017b) [accessed: 30.11.2017].
• Raoult V., Reid-Anderson S., Ferri A., Williamson J.E. 2017. How Reliable is Structure from Motion (SfM) over Time and between Observers? A Case Study Using Coral Reef Bommies. Remote Sensing, 9(740), 15pps. doi:10.3390/rs9070740.
• Rau J.Y., Jhan J.P., Hsu Y.C. 2015. Analysis of oblique aerial images for land cover and point cloud classification in an urban environment. IEEE Trans. Geosci. Remote Sens., 53, 1304–1319.
• Ren H., Zhao Y., Xiao W., Wang X., Sui T. 2020. An Improved Ground Control Point Configuration for Digital Surface Model Construction in a Coal Waste Dump Using an Unmanned Aerial Vehicle System. Remote Sensing, 12(10), 1623. MDPI AG. http://dx.doi.org/10.3390/rs12101623
• Smith M.W., Vericat D. 2015. From experimental plots to experimental landscapes: topography, erosion and deposition in sub‐humid badlands from structure‐from‐motion photogrammetry. Earth Surface Processes and Landforms, 40(12), 1656–1671.
• Smith M.W., Carrivick J.L., Quincey D.J. 2016. Structure from motion photogrammetry in physical geography. Progress in Physical Geography, 40(2), 247–275.
• Snavely N. 2008. Scene reconstruction and visualization from Internet photo collections, unpublished PhD thesis. University of Washington, USA.
• Snavely N., Seitz S.N., Szeliski R. 2008. Modeling the world from internet photo collections. International Journal of Computer Vision, 80, 189–210.
• Vacca G., Dessi A., Sacco A. 2017. The Use of Nadir and Oblique UAV Images for Building Knowledge. ISPRS Int. J. Geo-Inf., 6, 393: doi:10.3390/ijgi6120393www.mdpi.com/journal/ijgi
• Verykokou S., Ioannidis C. 2018. Oblique aerial images: A review focusing on georeferencing procedures. Int. J. Remote Sens., 39, 3452–3496.
• Wackrow R., Chandler J.H. 2008. A convergent image configuration for DEM extraction that minimises the systematic effects caused by an inaccurate lens model. Photogramm. Rec., 23, 6–18.
• Washburn M. 2017. Digital Terrain Model Generation using Structure from Motion: Influence of Canopy Closure and Interpolation Method on Accuracy. M.Sc. Thesis, Texas State University, USA.
• Westoby M.J., Brasington J., Glasser N.F., Hambrey M.J., Reynolds J.M. 2012. Structure-from-Motion photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology, 179, 300–314.
• Westoby M.J., Dunning S.A., Woodward J., Hein A.S., Marrero S.M., Winter K., Sugden D.E. 2015. Instruments and methods – Sedimentological characterization of Antarctic moraines using UAVs and Structure-from-Motion photogrammetry. Journal of Glaciology, 61(230). doi: 10.3189/2015JoG15J086.
• Wróżyński R., Pyszny K., Sojka M., Przybyła C., Murat-Błażejewska S. 2017. Ground volume assessment using Structure from Motion photogrammetry with a smartphone and a compact camera. Open Geosciences, 9, 281–294.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).