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Analysis of the impact of interior orientation parameters in different UAV-based image-block compositions on positional accuracy

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Understanding the factors that influence the quality of unmanned aerial vehicle (UAV)-based products is a scientifically ongoing and relevant topic. Our research focused on the impact of the interior orientation parameters (IOPs) on the positional accuracy of points in a calibration field, identified and measured in an orthophoto and a point cloud. We established a calibration field consisting of 20 materialized points and 10 detailed points measured with high accuracy. Surveying missions with a fixed-wing UAV were carried out in three series. Several image blocks that differed in flight direction (along, across), flight altitude (70 m, 120 m), and IOPs (known or unknown values in the image-block adjustment) were composed. The analysis of the various scenarios indicated that fixed IOPs, computed from a good geometric composition, can especially improve vertical accuracy in comparison with self-calibration; an image block composed from two perpendicular flight directions can yield better results than an image block composed from a single flight direction.
Rocznik
Strony
617--629
Opis fizyczny
Bibliogr. 27 poz., rys., tab., wykr., wzory
Twórcy
autor
  • University of Ljubljana, Faculty of Civil and Geodetic Engineering, Jamova cesta 2, SI-1000 Ljubljana, Slovenia
autor
  • DEZIS d.o.o., Goriška cesta 12, SI-5270 Ajdovščina, Slovenia
autor
  • C-ASTRAL, Tovarniška cesta 26, SI-5270 Ajdovščina, Slovenia
  • University of Ljubljana, Faculty of Civil and Geodetic Engineering, Jamova cesta 2, SI-1000 Ljubljana, Slovenia
  • University of Ljubljana, Faculty of Civil and Geodetic Engineering, Jamova cesta 2, SI-1000 Ljubljana, Slovenia
  • University of Ljubljana, Faculty of Civil and Geodetic Engineering, Jamova cesta 2, SI-1000 Ljubljana, Slovenia
Bibliografia
  • [1] Toth, C., Jóźków, G. (2016). Remote sensing platforms and sensors: A survey. ISPRS J. Photogramm. Remote Sens. , 115, 22-36.
  • [2] Colomina, I., Molina, P. (2014). Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS J. Photogramm. Remote Sens., 92, 79-97.
  • [3] Nex, F., Remondino, F. (2014). UAV for 3D mapping applications: a review. textitAppl. Geomat., 6(1), 1-15.
  • [4] Nasrullah, A.R. (2016). Systematic Analysis of Unmanned Aerial Vehicle (UAV) Derived Product Quality . M.Sc. Thesis. Faculty of Geo-Information Science and Earth Observation of the University of Twente, Enschede, The Netherlands.
  • [5] Gerke, M., Nex, F., Remondino, F., Jacobsen, K., Kremer, J., Karel, W., Hu, H., Ostrowski, W. (2016). Orientation of oblique airborne image sets - experiences from the ISPRS/EuroSDR benchmark on multi-platform photogrammetry. ISPRS - Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., XLI-B1, 185-191.
  • [6] Mesas-Carrascosa, J.F., Rumbao, C.I., Berrocal, A.J., Porras, G.A. (2014). Positional Quality Assessment of Orthophotos Obtained from Sensors Onboard Multi-Rotor UAV Platforms. Sensors, 14(12), 22394-22407.
  • [7] Shahbazi, M., Sohn, G., Théau, J., Menard, P. (2015). Development and Evaluation of a UAV-Photogrammetry System for Precise 3D Environmental Modeling. Sensors, 15(11), 27493-27524.
  • [8] Mesas-Carrascosa, F.J., Notario García, D.M., Merońo de Larriva, E.J., García-Ferrer, A. (2016). An Analysis of the Influence of Flight Parameters in the Generation of Unmanned Aerial Vehicle (UAV) Orthomosaicks to Survey Archaeological Areas. Sensors, 16(11), 1838-1851.
  • [9] Wierzbicki, D., Kedzierski, M., Fryskowska, A. (2015). Assessment of the Influence of UAV Image Quality on the Orthophoto Production. Int Arch Photogramm Remote Sens Spat. Inf Sci, XL-1/W4, 1-8.
  • [10] Kosmatin Fras, M., Kerin, A., Mesarič, M., Peterman, V., Grigillo, D. (2016). Assessment of The Quality of Digital Terrain Model Produced from Unmanned Aerial System Imagery. Int Arch Photogramm Remote Sens Spat. Inf Sci, XLI-B1, 893-899.
  • [11] Remondino, F., Stylianidis, E. (2016). Cultural Heritage Documentation with RPAS/UAV. In 3D Recording, Documentation and Management of Cultural Heritage, Stylianidis, E., Remondino, F., Eds. Dunbeath: Whittles Publishing, 369-379.
  • [12] El Meouche, R., Hijazi, I., Poncet, P.A., Abunemeh, M., Rezoug, M. (2016). UAV Photogrammetry Implementation to Enhance Land Surveying, Comparisons and Possibilities. Int Arch Photogramm Remote Sens Spat. Inf Sci, XLII-2/W2, 107-114.
  • [13] Kraft, T., Geßner, M., Meißner, H., Cramer, M., Gerke, M., Przybilla, H.J. (2016). Evaluation of a Metric Camera System Tailored for High Precision UAV Applications. Int Arch Photogramm Remote Sens Spat. Inf Sci, XLI-B1, 901-907.
  • [14] Vautherin, J., Rutishauser, S., Schneider-Zapp, K., Choi, H.F., Chovancova, V., Glass, A., Strecha, C. (2016). Photogrammetric Accuracy and Modeling of Rolling Shutter Cameras. ISPRS Ann Photogramm Remote Sens Spat. Inf Sci, III-3, 139-146.
  • [15] Kraus, K. (2007). Photogrammetry, Geometry from Images and Laser Scans. 2nd ed. Berlin: Walter de Gruyter.
  • [16] Lingua, A., Marenchino, D., Nex, F. (2009). Performance Analysis of the SIFT Operator for Automatic Feature Extraction and Matching in Photogrammetric Applications. Sensors, 9(5), 3745-3766.
  • [17] Kerner, S., Kaufman, I., Raizman, Y. (2016). Role of Tie-Points Distribution in Aerial Photography. Int Arch Photogramm Remote Sens Spat. Inf Sci, XL-3/W4, 41-44.
  • [18] Remondino, F., Nocerino, E., Toschi, I., Menna, F. (2017). A critical review of automated photogrammetric processing of large datasets. ISPRS - Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., XLII-2/W5, 591-599.
  • [19] Leica Geosystems Carriers. http://leica-geosystems.com/en/products/gnss-systems/accessories/carriers. (Aug, 2017).
  • [20] Luhmann, T., Fraser, C., Maas, H.G. (2016). Sensor modelling and camera calibration for close-range photogrammetry. ISPRS J. Photogramm. Remote Sens., 115, 37-46.
  • [21] Gašparovič, M., Gajski, D. (2016). Two-step Camera Calibration Method Developed for Micro UAV’s. Int Arch Photogramm Remote Sens Spat. Inf Sci, XLI-B1, 829-833.
  • [22] Tang, R., Fritsch, D. (2013). Correlation Analysis of Camera Self-Calibration in Close Range Photogrammetry. Photogramm. Rec., 28(141), 86-95.
  • [23] Chiang, K.W., Tsai, M.L., Chu, C.H. (2012). The Development of an UAV Borne Direct Georeferenced Photogrammetric Platform for Ground Control Point Free Applications. Sensors, 12(7), 9161-9180.
  • [24] Kaniewski P., Gil R., Konatowski S. (2017). Estimation of UAV Position with Use of Smoothing Algorithms. Metrol. Meas. Syst., 24(1), 127-142.
  • [25] Agisoft PhotoScan User Manual, Professional Edition, Version 1.2. http://www.agisoft.com/pdf/photoscan-pro_1_2_en.pdf. (Feb. 2016).
  • [26] Luhmann, T., Robson, S., Kyle, S., Boehm, J. (2014). Close-Range Photogrammetry and 3D Imaging. 2nd ed. Berlin/Boston: Walter de Gruyter.
  • [27] Tang, R. (2013). Mathematical methods for camera self-calibration in photogrammetry and computer vision. Ph.D. Thesis. University of Stuttgart, Germany.
Uwagi
EN
1. This research was partially founded by the Slovenian Research Agency within the frame of the research program P2-0227(A), Geoinformation Infrastructure and Sustainable Spatial Development of Slovenia.
PL
2. Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8afd8a7d-450e-44c5-aa10-cc9d7e5e7f61
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