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DOI
Warianty tytułu
Języki publikacji
Abstrakty
Unmanned aerial vehicles (UAVs) allow relatively cheap and fast acquisition of high-resolution data for small areas, making it possible to produce not only an orthophoto, understood as a two-dimensional product, but also a three-dimensional point cloud, which is the basis for obtaining a digital terrain model (DTM). The use of high-resolution image and elevation data may allow accurate shoreline delineation in areas where such measurement is not possible with other methods and further use of these data, e.g. for the production of electronic navigation charts. The accuracy of the final product, the DTM, is significantly affected by the photogrammetric classification process of the point cloud and the correct separation of the ground class. The aim of this study was to assess the effectiveness of the algorithms used to classify ground in photogrammetric point clouds and obtain correct DTMs. Three algorithms were tested: Adaptive Triangulated Irregular Network, Progressive Triangulated Irregular Network, and Multiscale Curvature Classification. The study involved two test areas covering natural shorelines. Images acquired with a UAV on the X8 system and a Sony alpha camera with a mounted 15 mm wide-angle lens were used as data for the research experiment. Accuracy analysis of the developed models was performed using check points acquired by the GNSS-RTK method.
Słowa kluczowe
Rocznik
Tom
Strony
108--118
Opis fizyczny
Bibliogr. 23 poz., rys., tab.
Twórcy
autor
- Maritime University of Szczecin, Faculty of Navigation, Department of Geoinformatics 46 Żołnierska St., 71-250 Szczecin, Poland
Bibliografia
- 1. Agisoft LLC (2021) Agisoft Metashape User Manual: Professional Edition, Version 1.7. [Online] Available from: https://www.agisoft.com/pdf/metashape-pro_1_7_en.pdf [Accessed: June 23, 2021].
- 2. Anders, N., Valente, J., Masselink, R. & Keesstra, S. 2019) Comparing filtering techniques for removing vegetation from UAV-based photogrammetric point clouds. Drones 3(3), 61, doi: 10.3390/drones3030061.
- 3. Axelsson, P. (1999) Processing of laser scanner data – Algorithms and applications. ISPRS Journal of Photogrammetry and Remote Sensing 54(2–3), pp. 138–147, doi: 10.1016/ S0924-2716(99)00008-8.
- 4. Axelsson, P. (2000) Peter Axelsson 110. International Archives of Photogrammetry and Remote Sensing 33(4), pp. 110–117.
- 5. Bakuła, K., Stȩpnik, M. & Kurczyński, Z. (2016) Influence of Elevation Data Source on 2D Hydraulic Modelling. Acta Geophysica 64(4), pp. 1176–1192, doi: 10.1515/acgeo2016-0030.
- 6. Blue Marble Geographics (2021) Global Mapper, Knowledge Base. [Online] Avaliable from: https://www.bluemarblegeo.com/knowledgebase/global-mapper-20-1/index.htm [Accessed: June 23, 2021].
- 7. Evans, J.S. & Hudak, A.T. (2007) A multiscale curvature algorithm for classifying discrete return LiDAR in forested environments. IEEE Transactions on Geoscience and Remote Sensing 45(4), pp. 1029–1038, doi: 10.1109/ TGRS.2006.890412.
- 8. Gruszczyński, W., Matwij, W. & Ćwiąkała, P. (2017) Comparison of low-altitude UAV photogrammetry with terrestrial laser scanning as data-source methods for terrain covered in low vegetation. ISPRS Journal of Photogrammetry and Remote Sensing 126, pp. 168–179. doi: 10.1016/j. isprsjprs.2017.02.015.
- 9. Jakovljevic, G., Govedarica, M, Alvarez-Taboada, F. & Pajic, V (2019) Accuracy assessment of deep learning based classification of LiDAR and UAV points clouds for DTM creation and flood risk mapping. Geosciences (Switzerland) 9(7), 323, doi: 10.3390/geosciences9070323.
- 10. Klápště, P., Fogl, M., Barták, V., Gdulová, K., Urban, R. & Moudrý, V. (2020) Sensitivity analysis of parameters and contrasting performance of ground filtering algorithms with UAV photogrammetry-based and LiDAR point clouds. International Journal of Digital Earth 13(8), doi: 10.1080/17538947.2020.1791267.
- 11. Liu, X. (2008) Airborne LiDAR for DEM generation: Some critical issues. Progress in Physical Geography 32(1), pp. 31–49, doi: 10.1177/0309133308089496.
- 12. Polat, N. & Uysal, M. (2015) Investigating performance of Airborne LiDAR data filtering algorithms for DTM generation. Measurement 63, pp. 61–68, doi: 10.1016/j.measurement.2014.12.017.
- 13. Rapidlasso GmbH (2021) LAStools software. [Online] Available from: https://rapidlasso.com/ [Accessed: June 23, 2021].
- 14. Salach, A., Bakuła, K., Pilarska, M., Ostrowski, W., Górski, K. & Kurczyński, Z. (2018) Accuracy assessment of point clouds from LiDAR and dense image matching acquired using the UAV platform for DTM creation. ISPRS International Journal of Geo-Information 7(9), 342, doi: 10.3390/ijgi7090342.
- 15. Sanz-Ablanedo, E., Chandler, J.H., Rodriguez-Pérez, J.R. & Ordóñez, C. (2018) 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 10(10), 1606, doi: 10.3390/ rs10101606.
- 16. Serifoglu Yilmaz, C. & Gungor, O. (2018) Comparison of the performances of ground filtering algorithms and DTM generation from a UAV-based point cloud. Geocarto International 33(5), pp. 522–537, doi: 10.1080/10106049. 2016.1265599.
- 17. Serifoglu Yilmaz, C., Yilmaz, V. & Güngör, O. (2018) Investigating the performances of commercial and non-commercial software for ground filtering of UAV-based point clouds. International Journal of Remote Sensing 39(15–16), pp. 5016–5042, doi: 10.1080/01431161.2017.1420942.
- 18. Sithole, G. & Vosselman, G. (2004) Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds. ISPRS Journal of Photogrammetry and Remote Sensing 59(1–2), pp. 85–101, doi: 10.1016/j.isprsjprs.2004.05.004.
- 19. Tan, Y., Wang, S., Xu, B. & Zhang, J. (2018) An improved progressive morphological filter for UAV-based photogrammetric point clouds in river bank monitoring. ISPRS Journal of Photogrammetry and Remote Sensing 146, pp. 421–429, doi: 10.1016/j.isprsjprs.2018.10.013.
- 20. Villanueva, J.K.S. & Blanco, A.C. (2019) Optimization of ground control point (GCP) configuration for unmanned aerial vehicle (UAV) survey using structure from motion (SFM). International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences – ISPRS Archives 42(4/W12), pp. 167–174, doi: 10.5194/isprs-archives -XLII-4-W12-167-2019.
- 21. Wallace, L., Lucieer, A., Malenovský, Z., Turner, D. & Vopĕnka, P. (2016) Assessment of forest structure using two UAV techniques: A comparison of airborne laser scanning and structure from motion (SfM) point clouds. Forests 7(3), 62, doi: 10.3390/f7030062.
- 22. Zeybek, M. & Şanlioğlu, İ. (2019) Point cloud filtering on UAV based point cloud. Measurement 133, pp. 99–111, doi: 10.1016/j.measurement.2018.10.013.
- 23. Zietara, A.M. (2017) Creating Digital Elevation Model (DEM) based on ground points extracted from classified aerial images obtained from Unmanned Aerial Vehicle (UAV). Master thesis. NTNU Norwegian University of Science and Technology (June).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-26147478-b6f4-4e47-9686-fc9a357e39fb
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