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İHA'larda Kamera Kalibrasyonun Ortofoto Konum Doğruluğuna Etkisi

Year 2023, Volume: 4 Issue: 1, 83 - 99, 28.03.2023

Cumhur Şahin

https://doi.org/10.48123/rsgis.1207512
Cited By: 1

Abstract

İnsansız hava araçları (İHA), pilotsuz otonom veya uzaktan kumandalı hava araçlarıdır. İHA'lar, metrik olmayan fotogrametrik ekipman taşıyabilen hava platformlarıdır. Bu çalışmada; DJI Phantom 4 Pro ekipmanı üzerinde bulunan kameranın iki farklı kalibrasyon değerinin iki farklı uçuş yüksekliğinden elde edilen ortofoto haritalara etkisi araştırılmıştır. Çalışma alanı olarak Gebze Teknik Üniversitesi kampüsü içerisinde bir alan seçilmiştir. Kameranın kalibrasyonu için PI 3000 yazılımı kullanılmış ve hesaplanan parametreler ile konvansiyonel parametreler arasındaki farklar belirlenmiştir. Ayrıca parametrelerin konum doğruluğuna etkisi araştırılmıştır. Stereo modelde yükseklik koh.’sı resim ölçeği, uçuş yüksekliği, baz uzunluğu ve resim koordinatlarının ölçme doğruluğuna bağlıdır. Resim koordinatları x, y nin ölçme doğruluğu da kalibrasyon doğruluğundan etkilendiği için Z değerinden bağımsız kalibrasyon alanı kullanılabilir. Arazi işleri, GPS ile üretilen ortofotoların coğrafi referans ve saha ölçümleri ve İHA'lar ile iki farklı yükseklikten çalışma alanının ölçümü. Büro çalışması, ortofotoların üretildiği, coğrafi referanslarının yapıldığı ve kontrol noktalarının GPS koordinatları ile analiz edildiği kısımdır. Elde edilen veriler ile alçak uçuş irtifasında kalibrasyonun karesel ortalama değerini düşürdüğü görülmüştür. Ancak 120 metre için benzer bir sonuç elde edilememiştir.

Keywords

İHA Fotogrametri Kamera kalibrasyonu Ortofoto

References

  • Abdallah, A., Ali, M. Z., Misic, J., & Misic, V. (2019). Efficient security scheme for disaster surveillance UAV communication networks. Information, 10(2), 43. doi: 10.3390/info10020043.
  • Brown, D. C. (1971). Close-range camera calibration. Photogrammetric Engineering, 37(8), 855-866.
  • 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.
  • Cramer, M., Przybilla, H. J., & Zurhorst, A. (2017, September). UAV Cameras: overview and geometric calibration benchmark. In International Conference on Unmanned Aerial Vehicles in Geomatics, 2017. Proceedings. (pp. 85-92). ISPRS.
  • Eisenbeiss, H., & Sauerbier, M. (2011). Investigation of UAV systems and flight modes for photogrammetric applications. Photogrammetric Record, 26(136), 400-421.
  • Gašparović, M., & Gajski, D. (2016, July). Two-step camera calibration method developed for micro UAV's. In XXIII ISPRS Congress, 2016. Proceedings. (pp. 829-833). ISPRS.
  • Greenwood, W. W., Lynch J. P., & Zekkos D. (2019). Applications of UAVs in civil infrastructure. Journal of Infrastructure Systems, 25(2), 9-15.
  • Hasheminasab, S. M. Zhou, T., LaForest, L. M., & Habib, A. (2021). Multiscale image matching for automated calibration of UAV-based frame and line camera systems. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 3133-3150.
  • Kolecki, J., Kuras, P., Pastucha, E., Pyka, K., & Sierka, M. (2020). Calibration of industrial cameras for aerial photogrammetric mapping. Remote Sensing, 12(19), 3130. doi:10.3390/rs12193130.
  • Kraus, K. (1993). Photogrammetry, Vol. 1: Fundamentals and Standard Processes. Bonn, Germany: Dümmlers.
  • Kršák, B., Blištan, P., Pauliková, A., Puškárová, P., Kovanic, L., Palková J., & Zeliznaková, V. (2016). Use of low-cost UAV photogrammetry to analyze the accuracy of a digital elevation model in a case study. Measurement, 91, 276-287.
  • Krull, W., Tobera, R., Willms, I., Essen, H., & Wahl, N. (2012). Early forest fire detection and verification using optical smoke, gas and microwave sensors. Procedia Engineering, 45, 584-594.
  • Li, C. C., Zhang, G. S., Lei, T. J., & Gong, A. (2011). Quick imageprocessing method of UAV without control points data in earthquake disaster area. Transactions of Nonferrous Metals Society of China, 21(3), 523-528.
  • Liu, X. F., Peng, Z. R., & Zhang L.Y. (2019). Real-time UAV rerouting for traffic monitoring with decomposition based multi-objective optimization. Journal of Intelligent & Robotic Systems, 94(2), 491-501.
  • 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.
  • Mozas-Calvache, A. T., Perez-Garcia, J. L., Cardenal-Escarcena, F. J., Mata-Castro, E., & Delgado-Garcia, J. (2012). Method for photogrammetric surveying of archaeological sites with light aerial platforms. Journal of Archaeological Science, 39(2), 521-530.
  • Niethammer, U., James, M. R., Rothmund, S., Travelletti, J., & Joswig, M. M. (2012). UAV-based remote sensing of the super-sauze landslide: Evaluation and results. Engineering Geology, 128, 2-11.
  • Pérez, J. A., Goncalves G. R., Rangel, J. M. G., & Ortega P. F. (2019). Accuracy and effectiveness of orthophotos obtained from low cost UASs video imagery for traffic accident scenes documentation. Advences in Engineering Software, 132, 47-54.
  • Pérez, M., Agüera, F., & Carvajal, F. (2011, September). Digital camera calibration using images taken from an unmanned aerial vehicle. In ISPRS Zurich 2011 Workshop, 2011. Proceedings. (pp. 167-171). ISPRS.
  • Roncella, R., & Forlani, G. (2021). UAV block geometry design and camera calibration: A simulation study. Sensors, 21(18), 6090. doi:10.3390/s21186090.
  • Simarro, G., Calvete, D., Plomaritis, T.A., Moreno-Noguer, F., Giannoukakou-Leontsini, I., Montes, J., & Durán, R. (2021). The influence of camera calibration on nearshore bathymetry estimation from UAV videos. Remote Sensing, 13(1), 150. doi: 10.3390/rs13010150.
  • Song, F., Dan, T., Yu, R., Yang, K., Yang, Y., Chen W. Y., Gao, X. Y., & Ong, S. H. (2019). Small UAV-based multi-temporal change detection for monitoring cultivated land cover changes in mountainous terrain. Remote Sensing Letters, 10(6), 575-581.
  • Stagakis, S., Gonzalez-Dugo, V., Cid, P., Gullien, M. L., & Zarco-Tejoda, P. J. (2012). Monitoring water stress and fruit quality in an orange orchard under regulated deficit irrigation using narrow-band structural and physiological remote sensing indices. ISPRS Journal of Photogrammetry and Remote Sensing, 71(2012), 47-61.
  • Takahashi, Y., & Chikatsu, H. (2015, May). Camera calibration for UAV application using sensor of mobile camera. In Indoor-Outdoor Seamless Modelling, Mapping and Navigation, 2015. Proceedings. (pp. 239-242). ISPRS.
  • Wang, F. L., Wang, F. M., Zhang, Y., Hu, J. H., Huang, J. F., & Xie, J. K. (2019). Rice yield estimation using parcel-level relative spectra variables from UAV-based hyperspectral imagery. Frontiers Plant Science, 10, 453. doi:10.3389/fpls.2019.00453.
  • Wierzbicki, D. (2018). Multi-camera imaging system for UAV photogrammetry. Sensors, 18(8), 2433. doi:10.3390/s18082433.
  • Wu, Z. C., Ni, M., Hu, Z. W., Wang, J. J., Li, Q. Q., & Wu, G. F. (2019). Mapping invasive plant with UAV-derived 3D mesh model in mountain area-A case study in Shenzhen Coast, China. International Journal of Applied Earth Observation and Geoinformation, 77, 129-139.
  • Zhang, N., Zhang, X. L., Yang, G. J., Zhu, C. H., Huo, L. N., & Feng, H. K. (2018). Assessment of defoliation during the Dendrolimus tabulaeformis Tsai et Liu disaster outbreak using UAV-based hyperspectral images. Remote Sensing of Enviroment, 217, 325-337.
  • Zhou, Y., Rupnik, E., Meynard, C., Thom, C., & Pierrot-Deseilligny, M. (2020). Simulation and analysis of photogrammetric UAV image blocks-influence of camera calibration error. Remote Sensing, 12(1), 22. doi:10.3390/rs12010022.

Effect of Camera Calibration Refreshing on Orthophoto Position Accuracy in UAV Mapping

Year 2023, Volume: 4 Issue: 1, 83 - 99, 28.03.2023

Cumhur Şahin

https://doi.org/10.48123/rsgis.1207512
Cited By: 1

Abstract

Unmanned aerial vehicles (UAVs) are autonomous or remote control controlled air vehicles without a pilot. UAVs are aerial platforms capable of carrying non-metric photogrammetric equipment. In this study; the effect of two different calibration values of the camera available on the DJI Phantom 4 Pro equipment to the ortho-photo maps obtained from two different flight heights was investigated. An area within the campus of Gebze Technical University was chosen as a study area. PI 3000 software was used to calibrate the camera and the differences between the calculated parameters and the conventional parameters were determined. Also, the effect of the parameters on position accuracy was investigated. In the photogrammetric stereo model, the rms of Z depends on the picture scale, flight height, base length and the measurement accuracy of image coordinates. Since the measurement accuracy of the image coordinates x, y is also affected by the calibration accuracy, the calibration field independent of the Z value can be used. Geo-referencing and field measurements of the orthophotos produced by the GPS and measurement of the work area from two different heights with UAVs. Office work is the part where orthophotos are produced, georeferenced and analyzed with GPS coordinates of control points. The data obtained in the study reduces the rms value when recalibration is performed at a low flight altitude. However, a similar result could not be obtained for 120 meters flight altitude.

Keywords

UAV Photogrammetry Camera calibration Orthophoto

References

  • Abdallah, A., Ali, M. Z., Misic, J., & Misic, V. (2019). Efficient security scheme for disaster surveillance UAV communication networks. Information, 10(2), 43. doi: 10.3390/info10020043.
  • Brown, D. C. (1971). Close-range camera calibration. Photogrammetric Engineering, 37(8), 855-866.
  • 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.
  • Cramer, M., Przybilla, H. J., & Zurhorst, A. (2017, September). UAV Cameras: overview and geometric calibration benchmark. In International Conference on Unmanned Aerial Vehicles in Geomatics, 2017. Proceedings. (pp. 85-92). ISPRS.
  • Eisenbeiss, H., & Sauerbier, M. (2011). Investigation of UAV systems and flight modes for photogrammetric applications. Photogrammetric Record, 26(136), 400-421.
  • Gašparović, M., & Gajski, D. (2016, July). Two-step camera calibration method developed for micro UAV's. In XXIII ISPRS Congress, 2016. Proceedings. (pp. 829-833). ISPRS.
  • Greenwood, W. W., Lynch J. P., & Zekkos D. (2019). Applications of UAVs in civil infrastructure. Journal of Infrastructure Systems, 25(2), 9-15.
  • Hasheminasab, S. M. Zhou, T., LaForest, L. M., & Habib, A. (2021). Multiscale image matching for automated calibration of UAV-based frame and line camera systems. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 3133-3150.
  • Kolecki, J., Kuras, P., Pastucha, E., Pyka, K., & Sierka, M. (2020). Calibration of industrial cameras for aerial photogrammetric mapping. Remote Sensing, 12(19), 3130. doi:10.3390/rs12193130.
  • Kraus, K. (1993). Photogrammetry, Vol. 1: Fundamentals and Standard Processes. Bonn, Germany: Dümmlers.
  • Kršák, B., Blištan, P., Pauliková, A., Puškárová, P., Kovanic, L., Palková J., & Zeliznaková, V. (2016). Use of low-cost UAV photogrammetry to analyze the accuracy of a digital elevation model in a case study. Measurement, 91, 276-287.
  • Krull, W., Tobera, R., Willms, I., Essen, H., & Wahl, N. (2012). Early forest fire detection and verification using optical smoke, gas and microwave sensors. Procedia Engineering, 45, 584-594.
  • Li, C. C., Zhang, G. S., Lei, T. J., & Gong, A. (2011). Quick imageprocessing method of UAV without control points data in earthquake disaster area. Transactions of Nonferrous Metals Society of China, 21(3), 523-528.
  • Liu, X. F., Peng, Z. R., & Zhang L.Y. (2019). Real-time UAV rerouting for traffic monitoring with decomposition based multi-objective optimization. Journal of Intelligent & Robotic Systems, 94(2), 491-501.
  • 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.
  • Mozas-Calvache, A. T., Perez-Garcia, J. L., Cardenal-Escarcena, F. J., Mata-Castro, E., & Delgado-Garcia, J. (2012). Method for photogrammetric surveying of archaeological sites with light aerial platforms. Journal of Archaeological Science, 39(2), 521-530.
  • Niethammer, U., James, M. R., Rothmund, S., Travelletti, J., & Joswig, M. M. (2012). UAV-based remote sensing of the super-sauze landslide: Evaluation and results. Engineering Geology, 128, 2-11.
  • Pérez, J. A., Goncalves G. R., Rangel, J. M. G., & Ortega P. F. (2019). Accuracy and effectiveness of orthophotos obtained from low cost UASs video imagery for traffic accident scenes documentation. Advences in Engineering Software, 132, 47-54.
  • Pérez, M., Agüera, F., & Carvajal, F. (2011, September). Digital camera calibration using images taken from an unmanned aerial vehicle. In ISPRS Zurich 2011 Workshop, 2011. Proceedings. (pp. 167-171). ISPRS.
  • Roncella, R., & Forlani, G. (2021). UAV block geometry design and camera calibration: A simulation study. Sensors, 21(18), 6090. doi:10.3390/s21186090.
  • Simarro, G., Calvete, D., Plomaritis, T.A., Moreno-Noguer, F., Giannoukakou-Leontsini, I., Montes, J., & Durán, R. (2021). The influence of camera calibration on nearshore bathymetry estimation from UAV videos. Remote Sensing, 13(1), 150. doi: 10.3390/rs13010150.
  • Song, F., Dan, T., Yu, R., Yang, K., Yang, Y., Chen W. Y., Gao, X. Y., & Ong, S. H. (2019). Small UAV-based multi-temporal change detection for monitoring cultivated land cover changes in mountainous terrain. Remote Sensing Letters, 10(6), 575-581.
  • Stagakis, S., Gonzalez-Dugo, V., Cid, P., Gullien, M. L., & Zarco-Tejoda, P. J. (2012). Monitoring water stress and fruit quality in an orange orchard under regulated deficit irrigation using narrow-band structural and physiological remote sensing indices. ISPRS Journal of Photogrammetry and Remote Sensing, 71(2012), 47-61.
  • Takahashi, Y., & Chikatsu, H. (2015, May). Camera calibration for UAV application using sensor of mobile camera. In Indoor-Outdoor Seamless Modelling, Mapping and Navigation, 2015. Proceedings. (pp. 239-242). ISPRS.
  • Wang, F. L., Wang, F. M., Zhang, Y., Hu, J. H., Huang, J. F., & Xie, J. K. (2019). Rice yield estimation using parcel-level relative spectra variables from UAV-based hyperspectral imagery. Frontiers Plant Science, 10, 453. doi:10.3389/fpls.2019.00453.
  • Wierzbicki, D. (2018). Multi-camera imaging system for UAV photogrammetry. Sensors, 18(8), 2433. doi:10.3390/s18082433.
  • Wu, Z. C., Ni, M., Hu, Z. W., Wang, J. J., Li, Q. Q., & Wu, G. F. (2019). Mapping invasive plant with UAV-derived 3D mesh model in mountain area-A case study in Shenzhen Coast, China. International Journal of Applied Earth Observation and Geoinformation, 77, 129-139.
  • Zhang, N., Zhang, X. L., Yang, G. J., Zhu, C. H., Huo, L. N., & Feng, H. K. (2018). Assessment of defoliation during the Dendrolimus tabulaeformis Tsai et Liu disaster outbreak using UAV-based hyperspectral images. Remote Sensing of Enviroment, 217, 325-337.
  • Zhou, Y., Rupnik, E., Meynard, C., Thom, C., & Pierrot-Deseilligny, M. (2020). Simulation and analysis of photogrammetric UAV image blocks-influence of camera calibration error. Remote Sensing, 12(1), 22. doi:10.3390/rs12010022.
There are 29 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Cumhur Şahin 0000-0003-1960-1992

Publication Date March 28, 2023
Submission Date November 20, 2022
Acceptance Date February 20, 2023
Published in Issue Year 2023 Volume: 4 Issue: 1

Cite

APA Şahin, C. (2023). Effect of Camera Calibration Refreshing on Orthophoto Position Accuracy in UAV Mapping. Türk Uzaktan Algılama Ve CBS Dergisi, 4(1), 83-99. https://doi.org/10.48123/rsgis.1207512

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