The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV) Imagery, Based on Structure from Motion (SfM) Point Clouds
Open accessAbstract
Unmanned Aerial Vehicles (UAVs) are an exciting new remote sensing tool capable of acquiring high resolution spatial data. Remote sensing with UAVs has the potential to provide imagery at an unprecedented spatial and temporal resolution. The small footprint of UAV imagery, however, makes it necessary to develop automated techniques to geometrically rectify and mosaic the imagery such that larger areas can be monitored. In this paper, we present a technique for geometric correction and mosaicking of UAV photography using feature matching and Structure from Motion (SfM) photogrammetric techniques. Images are processed to create three dimensional point clouds, initially in an arbitrary model space. The point clouds are transformed into a real-world coordinate system using either a direct georeferencing technique that uses estimated camera positions or via a Ground Control Point (GCP) technique that uses automatically identified GCPs within the point cloud. The point cloud is then used to generate a Digital Terrain Model (DTM) required for rectification of the images. Subsequent georeferenced images are then joined together to form a mosaic of the study area. The absolute spatial accuracy of the direct technique was found to be 65–120 cm whilst the GCP technique achieves an accuracy of approximately 10–15 cm.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472
Accurate, Dense, and Robust Multiview Stereopsis
Furukawa, Y., & Ponce, J. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.161 · Cited by 3,019
Modeling the World from Internet Photo Collections
Snavely, N., Seitz, S. M., & Szeliski, R. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0107-3 · Cited by 2,155
Berni, J., Zarco-Tejada, P. J., Suarez, L., & Fereres, E. (2009) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2008.2010457 · Cited by 1,308
Elements of Photogrammetry(with Applications in GIS)
Dewitt, B. A., & Wolf, P. R. (2000) · McGraw-Hill Higher Education eBooks · Cited by 660
Antarctic climate change and the environment
Convey, P., Bindschadler, R., di Prisco, G., Fahrbach, E., Gutt, J., Hodgson, D. A., Mayewski, P. A., Summerhayes, C. P., Turner, J., & the ACCE Consortium (2009) · Antarctic Science · doi:10.1017/s0954102009990642 · Cited by 571
Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring
Hunt, E. R., Hively, W. D., Fujikawa, S. J., Linden, D. S., Daughtry, C. S. T., & McCarty, G. W. (2010) · Remote Sensing · doi:10.3390/rs2010290 · Cited by 488
Lelong, C. C. D., Burger, P., Jubelin, G., Roux, B., Labbé, S., & Baret, F. (2008) · Sensors · doi:10.3390/s8053557 · Cited by 408
Laliberte, A. S., Goforth, M. A., Steele, C. M., et al. (2011) · Remote Sensing · doi:10.3390/rs3112529 · Cited by 322
Remote Sensing of Vegetation Structure Using Computer Vision
Dandois, J. P., & Ellis, E. C. (2010) · Remote Sensing · doi:10.3390/rs2041157 · Cited by 267
Cited by
Mancini, F., Dubbini, M., Gattelli, M., Stecchi, F., Fabbri, S., & Gabbianelli, G. (2013) · Remote Sensing · doi:10.3390/rs5126880 · Cited by 770
Agricultural remote sensing big data: Management and applications
Huang, Y., CHEN, Z.-X., YU, T., HUANG, X.-Z., & GU, X.-F. (2018) · Journal of Integrative Agriculture · doi:10.1016/s2095-3119(17)61859-8 · Cited by 448
Dandois, J., Olano, M., & Ellis, E. (2015) · Remote Sensing · doi:10.3390/rs71013895 · Cited by 435
Woodget, A. S., Carbonneau, P. E., Visser, F., & Maddock, I. P. (2015) · Earth Surface Processes and Landforms · doi:10.1002/esp.3613 · Cited by 424
Holman, F., Riche, A., Michalski, A., Castle, M., Wooster, M., & Hawkesford, M. (2016) · Remote Sensing · doi:10.3390/rs8121031 · Cited by 398
Metadata
- Authors
- Turner, D., Lucieer, A., & Watson, C.
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2012-05-14
- Volume
- 4
- Issue
- 5
- Pages
- 1392-1410
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W2022591200
- Cited by
- 744
- References
- 22
- FWCI
- 54.26
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV) Imagery, Based on Structure from Motion (SfM) Point Clouds. https://app.cochat.ai/papers/10.3390/rs4051392
- The paper
- Turner, D., Lucieer, A., & Watson, C. (2012). An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV) Imagery, Based on Structure from Motion (SfM) Point Clouds. Remote Sensing. https://doi.org/10.3390/rs4051392
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
