357
Views
2
CrossRef citations to date
0
Altmetric
Research Articles

Multi-temporal orthophoto and digital surface model registration produced from UAV imagery over an agricultural field

, , , , &
Pages 18767-18790 | Received 16 Mar 2022, Accepted 31 Oct 2022, Published online: 16 Nov 2022

References

  • Aicardi I, Nex F, Gerke M, Lingua A. 2016. An image-based approach for the co-registration of multi-temporal UAV image datasets. Remote Sens. 8(9):779.
  • Bastiaanssen WGM, Molden DJ, Makin IW. 2000. Remote sensing for irrigated agriculture: examples from research and possible applications. Agric WaterManag. 46(2):137–155.
  • Bay H, Ess A, Tuytelaars T, Van Gool L. 2008. Speeded-up robust features (SURF). Comput Vis Image Underst. 110(3):346–359.
  • Bentoutou Y, Taleb N, Kpalma K, Ronsin J. 2005. An automatic image registration for applications in remote sensing. IEEE Trans Geosci Remote Sens. 43(9):2127–2137.
  • Chang X, Du S, Li Y, Fang S. 2018. A coarse-to-fine geometric scale-invariant feature transform for large size high resolution satellite image registration. Sensors. 18(5):1360.
  • Chebrolu N, Labe T, Stachniss C. 2018. Robust long-term registration of UAV images of crop fields for precision agriculture. IEEE Robot Autom Lett. 3(4):3097–3104.
  • Chen B, Chen Z, Deng L, Duan Y, Zhou J. 2016. Building change detection with RGB-D map generated from UAV images. Neurocomputing. 208:350–364.
  • Choi HS, Kim EM. 2017. Image registration of drone images through association analysis of linear features. J Korean Soc Surv Geod Photogramm Cartogr. 35(6):441–452.
  • Du S, Wang M, Fang S. 2017. Block-and-octave constraint SIFT with multi-thread processing for VHR satellite image matching. Remote Sens Lett. 8(12):1180–1189.
  • Gomez C, Purdie H. 2016. UAV-based photogrammetry and geocomputing for hazards and disaster risk monitoring – a review. Geoenviron Disasters. 3(1):1–11.
  • Gong M, Zhao S, Jiao L, Tian D, Wang S. 2014. A novel coarse-to-fine scheme for automatic image registration based on SIFT and mutual information. IEEE Trans Geosci Remote Sens. 52(7):4328–4338.
  • Gruszczyński W, Puniach E, Ćwiąkała P, Matwij W. 2019. Application of convolutional neural networks for low vegetation filtering from data acquired by UAVs. ISPRS J Photogramm Remote Sens. 158:1–10.
  • Gruszczyński W, Puniach E, Cwiakala P, Matwij W. 2022. Correction of low vegetation impact on UAV-derived point cloud heights with U-Net networks. IEEE Trans Geosci Remote Sens. 60:1–18.
  • Han Y, Choi J, Byun Y, Kim Y. 2014. Parameter optimization for the extraction of matching points between high-resolution multisensor images in urban areas. IEEE Trans Geosci Remote Sens. 52(9):5612–5621.
  • Han Y, Choi J, Jung J, Chang A, Oh S, Yeom J. 2019. Automated coregistration of multisensor orthophotos generated from unmanned aerial vehicle platforms. J Sensors. 2019:1–10.
  • Han Y. 2017. Fine registration between very high resolution satellite images using registration noise distribution. J Korean Soc Surv Geod Photogramm Cartogr. 35(3):125–132.
  • Hasheminasab SM, Zhou T, Habib A. 2020. GNSS/INS-assisted structure from motion strategies for UAV-based imagery over mechanized agricultural fields. Remote Sens. 12(3):351.
  • Honkavaara E, Saari H, Kaivosoja J, Pölönen I, Hakala T, Litkey P, Mäkynen J, Pesonen L. 2013. Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight UAV spectral camera for precision agriculture. Remote Sens. 5(10):5006–5039.
  • Huo C, Pan C, Huo L, Zhou Z. 2012. Multilevel SIFT matching for large-size VHR image registration. IEEE Geosci Remote Sensing Lett. 9(2):171–175.
  • Kim DW, Yun H, Jeong SJ, Kwon YS, Kim SG, Lee W, Kim HJ. 2018. Modeling and testing of growth status for chinese cabbage and white radish with UAV-based RGB imagery. Remote Sens. 10(4):563.
  • Kim T, Han Y. 2021. Integrated preprocessing of multitemporal very-high-resolution satellite images via conjugate points-based pseudo-invariant feature extraction. Remote Sens. 13(19):3990.
  • Kim T, Lee K, Lee WH, Yeom J, Jun S, Han Y. 2019. Coarse to fine image registration of unmanned aerial vehicle images over agricultural area using SURF and mutual information methods. Korean J Remote Sens. 35(6–1):945–957.
  • Lee HS, Oh JH. 2021. Correcting digital elevation models (DEM) from unmanned aerial vehicles (UAV): a new method using polynomial model matching techniques. J Coastal Res. 114(SI):434–438.
  • Li J, Hu Q, Ai M, Zhong R. 2017. Robust feature matching via support-line voting and affine-invariant ratios. ISPRS J Photogramm Remote Sens. 132:61–76.
  • Li M, Shamshiri RR, Schirrmann M, Weltzien C, Shafian S, Laursen MS. 2022. UAV oblique imagery with an adaptive micro-terrain model for estimation of leaf area index and height of maize canopy from 3D point clouds. Remote Sens. 14(3):585.
  • Liu J, Xu W, Guo B, Zhou G, Zhu H. 2021. Accurate mapping method for UAV photogrammetry without ground control points in the map projection frame. IEEE Trans Geosci Remote Sens. 59(11):9673–9681.
  • Maes WH, Steppe K. 2019. Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture. Trends Plant Sci. 24(2):152–164.
  • Mulla DJ. 2013. Twenty five years of remote sensing in precision agriculture: key advances and remaining knowledge gaps. Biosyst Eng. 114(4):358–371.
  • Na S, Park C, So K, Ahn H, Lee K. 2018. Application method of unmanned aerial vehicle for crop monitoring in Korea. Korean J Remote Sens. 34(5):829–846.
  • Nex F, Remondino F. 2014. UAV for 3D mapping applications: a review. Appl Geomat. 6(1):1–15.
  • Nongsaro. 2021. Information of crops varieties. 2021. Repub Korea Rural Dev Adm; [accessed 2020 May 27]. http://www.nongsaro.go.kr.
  • Oh J, Han Y. 2020. A double epipolar resampling approach to reliable conjugate point extraction for accurate kompsat-3/3A stereo data processing. Remote Sens. 12(18):2940.
  • Paul S, Pati UC. 2021. A comprehensive review on remote sensing image registration. Int J Remote Sens. 42(14):5396–5432.
  • Raeva PL, Šedina J, Dlesk A. 2019. Monitoring of crop fields using multispectral and thermal imagery from UAV. Eur J Remote Sens. 52(sup1):192–201.
  • Rokhmana CA. 2015. The potential of UAV-based remote sensing for supporting precision agriculture in indonesia. Procedia Environ Sci. 24:245–253.
  • Torres-Sánchez J, Peña JM, de Castro AI, López-Granados F. 2014. Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Comput Electron Agric. 103:104–113.
  • Tsai CH, Lin YC. 2017. An accelerated image matching technique for UAV orthoimage registration. ISPRS J Photogramm Remote Sens. 128:130–145.
  • Tsouros DC, Bibi S, Sarigiannidis PG. 2019. A review on UAV-based applications for precision agriculture. Inf. 10(11):349.
  • Uysal M, Toprak AS, Polat N. 2015. DEM generation with UAV photogrammetry and accuracy analysis in sahitler hill. Meas. 73:539–543.
  • Viola P, Wells WM. 1997. Alignment by maximization of mutual information. Int J Comput Vis. 24(2):137–154.
  • Wang C, Myint SW. 2015. A simplified empirical line method of radiometric calibration for small unmanned aircraft systems-based remote sensing. IEEE J Sel Top Appl Earth Observ Remote Sens. 8(5):1876–1885.
  • Wang C, Wang L, Liu L. 2014. Progressive mode-seeking on graphs for sparse feature matching. In: Proceeding of the European Conference on Computer Vision; Sep 6–12; Zurich. p.788–802.
  • Wei Z, Han Y, Li M, Yang K, Yang Y, Luo Y, Ong SH. 2017. A small UAV based multi-temporal image registration for dynamic agricultural terrace monitoring. Remote Sens. 9(9):904.
  • Woebbecke DM, Meyer GE, Von Bargen K, Mortensen DA. 1995. Color indices for weed identification under various soil, residue, and lighting conditions. Trans Am Soc Agric Eng. 38(1):259–269.
  • Xiang H, Tian L. 2011. Method for automatic georeferencing aerial remote sensing (RS) images from an unmanned aerial vehicle (UAV) platform. Biosyst Eng. 108(2):104–113.
  • Ye Z, Kang J, Yao J, Song W, Liu S, Luo X, Xu Y, Tong X. 2020. Robust fine registration of multisensor remote sensing images based on enhanced subpixel phase correlation. Sensors. 20(15):4338.
  • Yeom J, Jung J, Chang A, Ashapure A, Maeda M, Maeda A, Landivar J. 2019. Comparison of vegetation indices derived from UAV data for differentiation of tillage effects in agriculture. Remote Sens. 11(13):1548.
  • Yun H. 2017. Development of remote sensing technology for growth estimation of white radish and napa cabbage using UAV and RGB camera. [master’s thesis]. Seoul: Seoul National University.
  • Zhang C, Kovacs JM. 2012. The application of small unmanned aerial systems for precision agriculture: a review. Precision Agric. 13(6):693–712.
  • Zhang T, Zhao R, Chen Z. 2020. Application of migration image registration algorithm based on improved SURF in remote sensing image mosaic. IEEE Access. 8:163637–163645.
  • Zhang Y, Xia C, Zhang X, Cheng X, Feng G, Wang Y, Gao Q. 2021. Estimating the maize biomass by crop height and narrowband vegetation indices derived from UAV-based hyperspectral images. Ecol Indic. 129:107985.
  • Zhuo X, Koch T, Kurz F, Fraundorfer F, Reinartz P. 2017. Automatic UAV image geo-registration by matching UAV images to georeferenced image data. Remote Sens. 9(4):376.
  • Zitová B, Flusser J. 2003. Image registration methods: a survey. Image Vis Comput. 21(11):977–1000.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.