Abstract
Interest in using inexpensive Unmanned Aerial System (UAS) technology for topographic mapping has recently significantly increased. Small UAS platforms equipped with consumer grade cameras can easily acquire high-resolution aerial imagery allowing for dense point cloud generation, followed by surface model creation and orthophoto production. In contrast to conventional airborne mapping systems, UAS has limited ground coverage due to low flying height and limited flying time, yet it offers an attractive alternative to high performance airborne systems, as the cost of the sensors and platform, and the flight logistics, is relatively low. In addition, UAS is better suited for small area data acquisitions and to acquire data in difficult to access areas, such as urban canyons or densely built-up environments. The main question with respect to the use of UAS is whether the inexpensive consumer sensors installed in UAS platforms can provide the geospatial data quality comparable to that provided by conventional systems.
This study aims at the performance evaluation of the current practice of UAS-based topographic mapping by reviewing the practical aspects of sensor configuration, georeferencing and point cloud generation, including comparisons between sensor types and processing tools. The main objective is to provide accuracy characterization and practical information for selecting and using UAS solutions in general mapping applications. The analysis is based on statistical evaluation as well as visual examination of experimental data acquired by a Bergen octocopter with three different image sensor configurations, including a GoPro HERO3+ Black Edition, a Nikon D800 DSLR and a Velodyne HDL-32. In addition, georeferencing data of varying quality were acquired and evaluated. The optical imagery was processed by using three commercial point cloud generation tools. Comparing point clouds created by active and passive sensors by using different quality sensors, and finally, by different commercial software tools, provides essential information for the performance validation of UAS technology.
References
[1] Pajeres G. Overview and Current Status of Remote Sensing Applications Based on Unmanned Aerial Vehicles (UAVs). Photogrammetric Engineering & Remote Sensing 2015, 81(4), 281–329.10.14358/PERS.81.4.281Search in Google Scholar
[2] Haubeck K, Prinz T. A UAV-Based Low-Cost Stereo Camera System for Archaeological Surveys – Experiences from Doliche (Turkey). The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2013, XL-1 / W2, 195–200.10.5194/isprsarchives-XL-1-W2-195-2013Search in Google Scholar
[3] Niethammer U, Rothmund D, Schwaderer U, Zeman J, Joswig M. Open Source Image-Processing Tools For Low-Cost UAV-Based Landslide Investigations. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2011, XXXVIII-1 / C22, 161–6.10.5194/isprsarchives-XXXVIII-1-C22-161-2011Search in Google Scholar
[4] Congalton RG, Green K. Assessing the accuracy of remotely sensed data: principles and practices. CRC press, 2008.10.1201/9781420055139Search in Google Scholar
[5] ASPRS. New Standards for New Era: Overview of the 2015 ASPRS Positional Accuracy Standards for Digital Geospatial Data. Photogrammetric Engineering & Remote Sensing 2015, 81(3), 173–6. (Accessed October 19, 2015, at http://www.asprs.org/a/society/committees/standards/ASPRS_Positional_Accuracy_Standards_Edition1_Version100_November2014.pdf.)10.14358/PERS.81.3.A1-A26Search in Google Scholar
[6] Toth CK. The role of surface complexity in airborne LiDAR product error characterization. Proc ASPRS Annual Conference 2011, CD-ROM.Search in Google Scholar
[7] ASPRS Guidelines: Vertical Accuracy Reporting for Lidar Data, 2004. (Accessed October 19, 2015, at http://www.asprs.org/a/society/committees/standards/Vertical_Accuracy_Reporting_for_Lidar_Data.pdf.)Search in Google Scholar
[8] Colomina I. On Trajectory Determination for Photogrammetry and Remote Sensing: Sensors, Models and Exploitation. Proc Photogrammetric Week 2015, 131–42.Search in Google Scholar
[9] Ladai AD, Miller J. Point Cloud Generation from sUAS-Mounted iPhone Imagery: Performance Analysis. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2014, XL-1, 201–5.10.5194/isprsarchives-XL-1-201-2014Search in Google Scholar
[10] Whitehead K, Hugenholtz CH. Applying ASPRS Accuracy Standards to Surveys from Small Unmanned Aircraft Systems (UAS). Photogrammetric Engineering & Remote Sensing 2015, 81(10), 787–93.10.14358/PERS.81.10.787Search in Google Scholar
[11] Gini R, Pagliari D, Passoni D, Pinto L, Sona G, Dosso P. UAV Photogrammetry: Block Triangulation Comparisons. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2013, XL-1/W2, 157–62.10.5194/isprsarchives-XL-1-W2-157-2013Search in Google Scholar
[12] Pfeifer N, Glira P, Briese C. Direct georeferencing with on board navigation components of light weight UAV platforms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2012, XXXIX-B7, 487–92.10.5194/isprsarchives-XXXIX-B7-487-2012Search in Google Scholar
[13] Kuhnert KD, Kuhnert L. 2013. Light-weight sensor package for precision 3D measurement with micro UAVs e. g. power-line monitoring. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2013, XL-1 / W2, 235–40.Search in Google Scholar
[14] Takasu T. RTKLIB ver. 2.4. 2 Manual. Tokyo University of Marine Science and Technology, 2013.Search in Google Scholar
[15] Jozkow G, Toth CK. Georeferencing experiments with UAS imagery. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences 2014, II-1, 25–9.10.5194/isprsannals-II-1-25-2014Search in Google Scholar
[16] Rehak M, Mabillard R, Skaloud J. A micro-UAV with the capability of direct georeferencing. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2013, XL-1 / W2, 317–23.10.5194/isprsarchives-XL-1-W2-317-2013Search in Google Scholar
[17] Hirschmüller H. Stereo processing by semiglobal matching and mutual information. IEEE Transactions on Pattern Analysis and Machine Intelligence 2008, 30(2), 328–41.10.1109/TPAMI.2007.1166Search in Google Scholar PubMed
[18] Remondino F, Spera MG, Nocerino E, Menna F, Nex F, Gonizzi-Barsanti S. Dense image matching: comparisons and analyses. Proc Digital Heritage International Congress (DigitalHeritage) 2013, 1, 47–54.10.1109/DigitalHeritage.2013.6743712Search in Google Scholar
[19] Scharstein D, Szeliski R. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision 2002, 47(1–3), 7–42.10.1023/A:1014573219977Search in Google Scholar
[20] Seitz SM, Curless B, Diebel J, Scharstein D, Szeliski R. A Comparison and evaluation of multi-view stereo reconstruction algorithms. Proc IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2006, 1, 519–26.Search in Google Scholar
[21] Ostrowski S, Jozkow G, Toth CK, Vander Jagt B. Analysis of Point Cloud Generation from UAS Images. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences 2014, II-1, 45–51.10.5194/isprsannals-II-1-45-2014Search in Google Scholar
[22] Grejner-Brzezinska DA, Toth CK, Jozkow G. On Sensor Georeferencing and Point Cloud Generation with sUAS. Proc ION Pacific PNT Meeting 2015, 839–48.Search in Google Scholar
[23] He F, Habib A. Target-based and Feature-based Calibration of Low-cost Digital Cameras with Large Field-of-view. Proc ASPRS Annual Conference 2015, online.Search in Google Scholar
[24] Lari Z, Al-Rawabdeh A, He F, Habib A, El-Sheimy N. Region-based 3D surface reconstruction using images acquired by low-cost unmanned aerial systems. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2015, XL-1 / W4, 167–73.10.5194/isprsarchives-XL-1-W4-167-2015Search in Google Scholar
[25] Jozkow G, Vander Jagt B, Toth CK. Experiments with UAS Imagery for Automatic Modeling of Power Line 3D Geometry. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2015, XL-1 / W4, 403–409.10.5194/isprsarchives-XL-1-W4-403-2015Search in Google Scholar
© 2015 Walter de Gruyter GmbH, Berlin/Munich/Boston