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Impact of radiometric correction on the processing of UAV images

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Radiometric correction is a process that is often neglected when developing unmanned aerial vehicle (UAV) images. The aim of the work was to test the radiometric correction of images taken from a Parrot Sequoia+ camera mounted on UAV. Therefore, a script was written in Matlab environment to enable radiometric correction of the obtained images. The images were subjected to the correction process using the Matlab script and the commercial software Pix4D. The results were compared, and the study found a significant improvement in the radiometry in both cases. The computational process eliminated the influence of variable in-flight insolation caused by cloud cover. The software developed for the article was found to be as good as the commercial one.
Słowa kluczowe
Rocznik
Strony
5--14
Opis fizyczny
Bibliogr. 26 poz., rys.
Twórcy
  • Koszalin University of Technology, Department of Geodesy and Geoinformatics 2 Śniadeckich St., 75-453 Koszalin, Poland
autor
  • Maritime University of Szczecin, Department of Geodesy and Offshore Survey 46 Żołnierska St., 71-250 Szczecin, Poland
  • Koszalin University of Technology, Department of Geodesy and Geoinformatics 2 Śniadeckich St., 75-453 Koszalin, Poland
Bibliografia
  • 1. Aasen, H., Burkart, A., Bolten, A. & Bareth, G. (2015) Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance. ISPRS Journal of Photogrammetry and Remote Sensing 108, pp. 245–259.
  • 2. Adler, K. (2018) Radiometric correction of multispectral images collected by a UAV for phenology studies. Thesis for Master of Science. Uppsala, Sweden: Swedish University of Agricultural Sciences.
  • 3. Delavarpour, N., Koparan, C., Nowatzki, J., Bajwa, S. & Sun X. (2021) A Technical Study on UAV Characteristics for Precision Agriculture Applications and Associated Practical Challenges. Remote Sensing 13(6), 1204.
  • 4. Deng, L., Yan, Y., Gong, H., Duan, F. & Zhong, R. (2018) The effect of spatial resolution on radiometric and geometric performances of a UAV-mounted hyperspectral 2D imager. ISPRS Journal of Photogrammetry and Remote Sensing 144, pp. 298–314.
  • 5. Duan, S.-B., Li, Z.-L., Wu, H., Tang, B.-H., Ma, L., Zhao, E. & Li, C. (2014) Inversion of the PROSAIL model to estimate leaf area index of maize, potato, and sunflower fields from unmanned aerial vehicle hyperspectral data. International Journal of Applied Earth Observation and Geoinformation 26, pp 12–20.
  • 6. Gago, J., Douthe, C., Coopman, R.E., Gallego, P.P., Ribas-Carbo, M., Flexas, J., Escalona, J. & Medrano, H. (2015) UAVs challenge to assess water stress for sustainable agriculture. Agricultural Water Management 153, pp. 9–19.
  • 7. Geoportal (2022) https://mapy.geoportal.gov.pl [Accessed: March 09, 2022].
  • 8. Huang, Y., Thomson, S., Hoffmann, C., Lan, Y. & Fritz, B. (2013) Development and prospect of unmanned aerial vehicle technologies for agricultural production management. International Journal of Agricultural and Biological Engineering 6(3), pp. 1–10.
  • 9. Inoue, Y., Sakaiya, E., Zhu, Y. & Takahashi, W. (2012) Diagnostic mapping of canopy nitrogen content in rice based on hyperspectral measurements. Remote Sensing of Environment 126, pp. 210–221.
  • 10. Jin, X., Liu, S., Baret, F., Hemerlé, M. & Comar, A. (2017) Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sensing of Environment 198, pp. 105–114.
  • 11. Launay, M. & Guerif, M. (2005) Assimilating remote sensing data into a crop model to improve predictive performance for spatial applications. Agriculture, Ecosystems & Environment 111, pp. 321–339.
  • 12. Olsson, P-O., Vivekar, A., Adler, K., Garcia Millan, V. E., Koc, A., Alamrani, M. & Eklundh, L. (2021) Radiometric Correction of Multispectral UAS Images: Evaluating the Accuracy of the Parrot Sequoia Camera and Sunshine Sensor. Remote Sensing 13(4), 577.
  • 13. Osińska-Skotak, K. (2007) The importance of radiometric correction in satellite images processing (in Polish). Archiwum Fotogrametrii, Kartografii i Teledetekcji 17b, pp. 577–590.
  • 14. Parrot for Developers (2017a) SEQ AN 02 – How to correct vignetting in images. [Online]. Available from: https:// forum.developer.parrot.com/t/parrot-announcement-release-of-application-notes/5455?source_topic_id=6558 [Accessed: March 09, 2022].
  • 15. Parrot for Developers (2017b) SEQ AN 01 – Pixel to Irradiance. [Online]. Available from: https://forum.developer. parrot.com/t/parrot-announcement-release-of-application-notes/5455?source_topic_id=6558 [Accessed: March 09, 2022].
  • 16. Parrot for Developers (2017c) SEQ AN 04 – How to correct distortion in images. [Online]. Available from: https://forum. developer.parrot.com/t/parrot-announcement-release-ofapplication-notes/5455?source_topic_id=6558 [Accessed: March 09, 2022].
  • 17. Sankaran, S., Khot, L.R., Espinoza, C.Z., Jarolmasjed, S., Sathuvalli, V.R., Vandemark, G.J., Miklas, P.N., Carter, A.H., Pumphrey, M.O., Knowles, N.R. & Pavek, M.J. (2015) Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review. European Journal of Agronomy 70, pp. 112–123.
  • 18. Schott, J.R. (2007) Remote Sensing: The Image Chain Approach. Oxford University Press, USA.
  • 19. Schowengerdt, R.A. (2006) Remote Sensing: Models and Methods for Image Processing. Elsevier Science.
  • 20. Tagle, X. (2017) Study of radiometric variations in Unmanned Aerial Vehicle remote sensing imagery for vegetation mapping. Thesis for Master of Science. Iquitos, Loreto, Peru: Instituto de Investigaciones de la Amazonía Peruana. Available from: https://doi.org/10.13140/ RG.2.2.16940.36485.
  • 21. Teixeira, A.A.D., Mendes, C.W. Júnior, Bredemeier, C., Negreiros, M. & Aquino, R. (2020) Evaluation of the radiometric accuracy of images obtained by a sequoia multispectral camera. Engenharia Agrícola 40, 6, pp. 759 –768.
  • 22. Verger, A., Vigneau, N., Chéron, C., Gilliot, J.-M., Comar, A. & Baret, F. (2014) Green area index from an unmanned aerial system over wheat and rapeseed crops. Remote Sensing of Environment 152, pp. 654–664.
  • 23. Yang, G., Liu, J., Zhao, C., Li, Zhenhong, Huang, Y., Yu, H., Xu, B., Yang, X., Zhu, D., Zhang, X., Zhang, R., Feng, H., Zhao, X., Li, Zhenhai, Li, H. & Yang, H. (2017) Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives. Frontiers in Plant Science 8, doi: 10.3389/fpls.2017.01111.
  • 24. Zaman-Allah, M., Vergara, O., Araus, J.L., Tarekegne, A., Magorokosho, C., Zarco-Tejada, P.J., Hornero, A., Albà, A.H., Das, B., Craufurd, P., Olsen, M., Prasanna, B.M. & Cairns, J. (2015) Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize. Plant Methods 11, 35.
  • 25. Zarco-Tejada, P., Morales, A., Testi, L. & Villalobos, F. (2013) Spatio-temporal patterns of chlorophyll fluorescence and physiological and structural indices acquired from hyperspectral imagery as compared with carbon fluxes measured with eddy covariance. Remote Sensing of Environment 133, pp. 102–115.
  • 26. Zhou, X., Zheng, H.B., Xu, X.Q., He, J.Y., Ge, X.K., Yao, X., Cheng, T., Zhu, Y., Cao, W.X. & Tian, Y.C. (2017) Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery. ISPRS Journal of Photogrammetry and Remote Sensing 130, pp. 246–255.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-91f5667a-d49d-4379-bfcc-f5c6a92ea084
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