Abstract
Unmanned aerial vehicles (UAVs) or drones are lightly weighted platforms with numerous advantages and can acquire very high-resolution data rapidly with less expenditure. However, the processing of very high-resolution data acquired through the sensors mounted on the UAV platform is a major challenge, and the commercial UAV data processing software is expensive. These software packages require high-configuration hardware and follow the general workflow of conventional photogrammetric approach for image alignment, point cloud generation, mesh generation, DSM, and ortho-mosaic generation. The integration of computer vision algorithms into digital photogrammetric techniques has made it possible to process the high-resolution datasets quickly. A wide variety of open-source software utilities are available that can perform these tasks individually; however, an integrated solution is always desirable. An attempt is made to develop an open-source tool for photogrammetric processing of UAV data using open-source libraries in Python. The software provides a complete workflow for photogrammetric processing of UAV data ranging from camera calibration, point cloud generation from selective frames based on B/H ratio, direct georeferencing of generated point cloud and digital elevation model (DEM) generation. The developed software is compatible with both 32-bit and 64-bit operating systems and low configuration hardware.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12524-020-01237-x/MediaObjects/12524_2020_1237_Fig1_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12524-020-01237-x/MediaObjects/12524_2020_1237_Fig2_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12524-020-01237-x/MediaObjects/12524_2020_1237_Fig3_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12524-020-01237-x/MediaObjects/12524_2020_1237_Fig4_HTML.png)
Similar content being viewed by others
References
Aber, J. S., Aber, S. W., & Pavri, F. (2002). Unmanned small-format aerial photography from kites for acquiring large-scale, high-resolution, multiview-angle imagery. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, 34, 1–6.
Ali, M. E. O., Eliwa, M. A., Mohammed, A. A., & Abbbas, A. M. (2000). Accuray analysis for new close-range photogrammetric systems. In: Presented at international archives of photogrammetry and remote sensing (Vol. 33, Part B5). Amsterdam.
Bradski, G., Kaehler, A. (2008). Learning OpenCV: Computer vision with the OpenCV library. O'Reilly Media, Inc.
CloudCompare (version 2.11) [GPL software]. (2019). Retrieved from https://www.cloudcompare.org/.
Fotinopoulos, V. (2004). Baloon photogrammetry for archaeological surveys. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 34(Part 30). Retrieved from: https://www.isprs.org/proceedings/xxxv/congress/comm5/papers/606.pdf.
GUI Architectures. (2015). martinfowler.com [Online]. Retrieved May 24, 2015. https://martinfowler.com/eaaDev/uiArchs.html.
Hasegawa, H., Matsuo, K., Koarai, M., Watanabe, N., Masaharu, H., & Fukushima, Y. (2000). DEM accuracy and the base to height (B/H) ratio of stereo images. International Archives of Photogrammetry and Remote Sensing, 33(B4/1; PART 4), 356–359.
Jordahl, K. (2014). GeoPandas: Python tools for geographic data. Retrieved Jan 4, 2015. https://github.com/geopandas/geopandas.
Jütte, K. (2008). Vergleichverschiedener low-cost LuftbildaufnahmesystemesowieEinsatz von Drohnen: Grenzen und Möglichkeiten. In: BayerischeLandesanstaltfür Wald und Forstwirtschaft. Der gepixelte Wald—FachtagungzurForstlichenFernerkundung.
Krasner, G. E., & Pope, S. T. (1988). A cookbook for using the model-view controller user interface paradigm in smalltalk-80. Journal of Object-Oriented Programming, 1(3), 26–49.
Lourakis M., & Argyros A. (2004). The design and implementation of a generic sparse bundle adjustment software package based on the levenberg-Marquardt algorithm. Technical Report 340, Institute of Computer Science-FORTH, Heraklion.
Niethammer, U., Rothmund, S., James, M. R., Travelletti, J., & Joswig, M. (2010). Uav—Based remote sensing of landslides. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences Commission V Symposium, 38(5), 496–501.
Przybilla, H. J., & Wester-Ebbinghaus, W. (1979). Aerial photos by means of radio-controlled aircraft. Bildmessung Und Luftbildwesen, 47(5), 137–142.
Snavely, N., Seitz, S. M., & Szeliski, R. (2006). Photo tourism: exploring photo collections in 3D. ACM Transactions on Graphics (TOG), 25, 835–846.
Snavely, N., Seitz, S. M., & Szeliski, R. (2008). Modeling the world from internet photo collections. International Journal of Computer Vision, 80(2), 189–210.
The Pyexiv2 Library. (2015). Pyexiv2, a python binding to exiv2. Retrieved Jan 4, 2015. https://tilloy.net/dev/pyexiv2.
Warmerdam, F. (2008). The geospatial data abstraction library. In G. B. Hall & M. G. Leahy (Eds.), Open source approaches in spatial data handling. Advances in Geographic Information Science (Vol. 2). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-540-74831-1_5.
Yang, M.-D., Chao, C.-F., Huang, K.-S., Lu, L.-Y., & Chen, Y.-P. (2013). Image-based 3D scene reconstruction and exploration in augmented reality. Automation in Construction, 33, 48–60.
Zhou, G., & Wu, J. (2006, May). Unmanned aerial vehicle (UAV) data flow processing for natural disaster response. In: ASPRS 2006 annual conference, Reno, Nevada. Retrived from: https://www.asprs.org/a/publications/proceedings/reno2006/0187.pdf.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Sharma, M., Raghavendra, S. & Agrawal, S. Development of an Open-Source Tool for UAV Photogrammetric Data Processing. J Indian Soc Remote Sens 49, 659–664 (2021). https://doi.org/10.1007/s12524-020-01237-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12524-020-01237-x