DOI: https://doi.org/10.14710/geoplanning.5.1.115-130

APPLICATION OF UAV WITH FISH-EYE LENSES CAMERA FOR 3D SURFACE MODEL RECONSTRUCTION

*Nugroho Purwono  -  Geospatial Information Agency of Indonesia, Indonesia
Agung Syetiawan  -  Geospatial Information Agency of Indonesia, Indonesia

Citation Format:
Abstract

Application of Unmanned Aerial Vehicles (UAV) for images acquisiton has been widely applied in survey and mapping. One of non-metric camera as the sensor that can be mounted on the UAV is fish-eye lenses. Fish-eye lenses camera provides images with wide range coverage. However these images are distorted and make them more difficult to use for mapping or 3D modelling. This research is aimed to make a 3D surface model by images reconstruction and to estimate the geolocation accuracy of the model generated by UAV images processing. As the approach of the method, combines the automation of computer vision technique with the photogrammetric grade accuracy. The complete photogrammetric workflow implemented in Pix4D Mapper. Meanwhile, UAV platform used is DJI Phantom 2 Vision+. Sample location in this research is an area of Geospatial Laboratorium in Parangtritis, Yogyakarta. The covered area in this research is 3.934 Ha. From the results of 186 images obtained 2.47 cm value of average Ground Sampling Distance (GSD). Moreover the numbers of 3D points for Bundle Block Images Adjustment are 243,373 points with 0.4348 value of Mean Reprojection Error (pixels). The results of 3D Densified Points are 6,207,780 and 101.04 points of average density per-m3. Generally, geolocation acuracy of the model produced by using this method is between 2.47 - 4.94 cm. Thus, it can be concluded that UAV with fish-eye lenses camera can be used to reconstruct 3D surface model. However, images correction and calibration should be required to produce an accurate 3D model.

Fulltext View|Download
Keywords: UAV; Fish-eye lenses; image processing; 3D surface model; model accuracy

Article Metrics:

Article Info
Section: Original Research
Language : EN
Recent articles
DROUGHT HAZARD CHARACTERISTIC USING SOIL MOISTURE DEFICIT INDEX MODELLING SPATIAL EXPLICIT MODELING TO UNDERSTAND THE DYNAMICS OF LANDUSE SWITCH USING OPEN SOURCE SATELLITE DATA ESTIMATING MANGROVE FOREST DENSITY USING GAP FRACTION METHOD AND VEGETATION TRANSFORMATION INDICES APPROACH More recent articles
Most cited articles
RELATIONSHIP BETWEEN URBANIZATION AND DENGUE HAEMORRHAGIC FEVER INCIDENCE IN SEMARANG CITY MONITORING THE LAND USE CHANGE IN CAMPUS 2 STKIP PGRI PONTIANAK POTENTIAL OF BIG DATA FOR PRO-ACTIVE PARTICIPATORY LAND USE PLANNING A GIS BASED EVALUATION OF LAND USE CHANGES AND ECOLOGICAL CONNECTIVITY INDEX TRANSFORMASI HUNIAN DENGAN PERSPEKTIF SPASIAL DAN TATANAN BUDAYA: KOMPARASI PERMUKIMAN KUMUH BANG BUA, THAILAND DAN KAMPUNG NAGA, INDONESIA More cited articles

  1. Aber, J. S., Marzolff, I., & Ries, J. B. (2010). Small-format aerial photography: Principles, techniques and geoscience applications. The Photogrammetric Record (pp. 15–22). Elsevier. [https://doi.org/10.1016/b978-0-444-53260-2.10002-x">Crossref]

  2. Azmi, S. M., Ahmad, B., & Ahmad, A. (2014). Accuracy assessment of topographic mapping using UAV image integrated with satellite images. IOP Conference Series: Earth and Environmental Science, 18. [https://doi.org/10.1088/1755-1315/18/1/012015">Crossref]

  3. Bandara, K. R. M. U., Samarakoon, L., Shrestha, R. P., & Kamiya, Y. (2011). Automated Generation of Digital Terrain Model using Point Clouds of Digital Surface Model in Forest Area. Remote Sensing, 3(5), 845–858. [https://doi.org/10.3390/rs3050845">Crossref]  

  4. Bemis, S. P., Micklethwaite, S., Turner, D., James, M. R., Akciz, S., Thiele, S. T., & Bangash, H. A. (2014). Ground-based and UAV-Based photogrammetry: A multi-scale, high-resolution mapping tool for structural geology and paleoseismology. Journal of Structural Geology, 69, 163–178. [https://doi.org/10.1016/j.jsg.2014.10.007">Crossref] 

  5. Clapuyt, F., Vanacker, V., & Oost, K. Van. (2016). Reproducibility of UAV-based earth topography reconstructions based on Structure-from-Motion algorithms. Geomorphology, 260, 4–15. [https://doi.org/10.1016/j.geomorph.2015.05.011">Crossref]

  6. Colomina, I., & Molina, P. (2014). Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 92, 79–97. [https://doi.org/10.1016/j.isprsjprs.2014.02.013">Crossref]

  7. Cook, K. L. (2017). An evaluation of the effectiveness of low-cost UAVs and structure from motion for geomorphic change detection. Geomorphology, 278, 195–208. [https://doi.org/10.1016/j.geomorph.2016.11.009">Crossref]

  8. Eisenbeiß, H., Zurich, E. T. H., Eisenbeiß, H., & Zürich, E. T. H. (2009). UAV photogrammetry. Institute of Photogrammetry and Remote Sensing. Zurich. [http://doi.org/doi.:10.3929/ethz-a-005939264">Crossref]

  9. Felipe-Garc’ia, B., Hernández-López, D., & Lerma, J. L. (2012). Analysis of the ground sample distance on large photogrammetric surveys. Applied Geomatics, 4(4), 231–244. [https://doi.org/10.1007/s12518-012-0084-2">Crossref] 

  10. Forsman, M. (2010). Point cloud densification.

  11. Gini, R., Pagliari, D., Passoni, D., Pinto, L., Sona, G., & Dosso, P. (2013). UAV PHOTOGRAMMETRY: BLOCK TRIANGULATION COMPARISONS. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-1/W2, 157–162. [https://doi.org/10.5194/isprsarchives-xl-1-w2-157-2013">Crossref]

  12. Gularso, H., Rianasari, H., & Silalahi, F. E. S. (2015). Penggunaan foto udara format kecil menggunakan wahana udara nir-awak dalam pemetaan skala besar. GEOMATIKA, 21(1), 37–44.

  13. Gurtner, A., Greer, D. G., Glassock, R., Mejias, L., Walker, R. A., & Boles, W. W. (2009). Investigation of Fish-Eye Lenses for Small-UAV Aerial Photography. IEEE Transactions on Geoscience and Remote Sensing, 47(3), 709–721. [https://doi.org/10.1109/tgrs.2008.2009763">Crossref]

  14. Han, J., Xu, Y., Di, L., & Chen, Y. (2013). Low-cost multi-UAV technologies for contour mapping of nuclear radiation field. Journal of Intelligent & Robotic Systems, 70(1–4), 401–410.  [https://doi.org/10.1007/s10846-012-9722-5">Crossref] 

  15. Kannala, J., & Brandt, S. S. (2006). A Generic Camera Model and Calibration Method for Conventional, Wide-angle and Fisheye Lens. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(8), 1335–1340. [https://doi.org/10.1109/tpami.2006.153">Crossref]

  16. Kersten, T., Haering, S., & AG, S. V. (1997). Efficient automated digital aerial triangulation through customisation of a commercial photogrammetric system. International Archives of Photogrammetry and Remote Sensing, 32(Part 3), 72–79.

  17. Kršák, B., Blišťan, P., Pauliková, A., Puškárová, P., Kovanič, L., Palková, J., & Zelizňaková, V. (2016). Use of low-cost UAV photogrammetry to analyze the accuracy of a digital elevation model in a case study. Measurement: Journal of the International Measurement Confederation,  91, 276–287. [https://doi.org/10.1016/j.measurement.2016.05.028">Crossref]

  18. Küng, O., Strecha, C., Fua, P., Gurdan, D., Achtelik, M., Doth, K.-M., & Stumpf, J. (2012). Simplified Building Models Extraction From Ultra-Light Uav Imagery. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVIII-, 217–222. [https://doi.org/10.5194/isprsarchives-xxxviii-1-c22-217-2011">Crossref]

  19. Luhmann, T., Fraser, C., & Maas, H.-G. (2016). Sensor modelling and camera calibration for close-range photogrammetry. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 37–46. [https://doi.org/10.1016/j.isprsjprs.2015.10.006">Crossref]

  20. Nex, F., & Remondino, F. (2013). UAV for 3D mapping applications: a review. Applied Geomatics, 6(1), 1–15. [https://doi.org/10.1007/s12518-013-0120-x">Crossref]

  21. Niederheiser, R., Mokroš, M., Lange, J., Petschko, H., Prasicek, G., & Elberink, S. O. (2016). Deriving 3D Point Clouds from Terrestrial Photographs - Comparison of different sensors and software. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B5, 685–692. [https://doi.org/10.5194/isprsarchives-xli-b5-685-2016">Crossref]

  22. Remondino, F., Barazzetti, L., Nex, F., Scaioni, M., & Sarazzi, D. (2012). UAV Photogrammetry for Mapping and 3D Modeling Current Status and Future Perspectives. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVIII-, 25–31. [https://doi.org/10.5194/isprsarchives-xxxviii-1-c22-25-2011">Crossref]

  23. Schneider, D., Schwalbe, E., & Maas, H.-G. (2009). Validation of geometric models for fisheye lenses. ISPRS Journal of Photogrammetry and Remote Sensing, 64(3), 259–266. [https://doi.org/10.1016/j.isprsjprs.2009.01.001">Crossref]

  24. Schwalbe, E. (2005). Geometric modelling and calibration of fisheye lens camera systems. Institute of Photogrammetry and Remote Sensing-Dresden University of Technology, Dresden. In Camera (Vol. XXXVI, p. Part 5/W8).

  25. Strecha, C. (2011). Automated photogrammetric techniques on ultra-light UAV imagery. Proc. of the 53rd Photogrammetric Week, Institut Für Photogrammetrie, Universität Stuttgart, 289–294.

  26. Strecha, C., Zoller, R., Rutishauser, S., Brot, B., Schneider-Zapp, K., Chovancova, V., … Glassey, L. (2014). Terrestrial 3D Mapping using Fisheye and Perspective Sensors.

  27. Wang, J., & Li, C. (2008). Acquisition of UAV images and the application in 3D city modeling. In International Symposium on Photoelectronic Detection and Imaging 2007: Image Processing (Vol. 6623). [https://doi.org/10.1117/12.791426">Crossref]


Last update:

No citation recorded.

Last update: 2024-11-05 09:19:09

No citation recorded.