Direct Georeferencing of Ultrahigh-Resolution UAV Imagery

By Darren Turner, Arko Lucieer, Luke Wallace. IEEE Transactions on Geoscience and Remote Sensing, 2014

371 citations recorded by the source corpus.

Micro-unmanned aerial vehicles often collect a large amount of images when mapping an area at an ultrahigh resolution. A direct georeferencing technique potentially eliminates the need for ground control points. In this paper, we developed a camera-global positioning system (GPS) module to allow the synchronization of camera exposure with the airframe's position as recorded by a GPS with 10-20-cm accuracy. Lever arm corrections were applied to the camera positions to account for the positional difference between the GPS antenna and the camera center. Image selection algorithms were implemented to eliminate blurry images and images with excessive overlap. This study compared three different software methods (Photoscan, Pix4D web service, and an in-house Bundler method). We evaluated each based on processing time, ease of use, and the spatial accuracy of the final mosaic produced. Photoscan showed the best performance as it was the fastest and the easiest to use and had the best spatial accuracy (average error of 0.11 m with a standard deviation of 0.02 m). This accuracy is limited by the accuracy of the differential GPS unit (10-20 cm) used to record camera position. Pix4D achieved a mean spatial error of 0.24 m with a standard deviation of 0.03 m, while the Bundler method had the worst mean spatial accuracy of 0.76 m with a standard deviation of 0.15 m. The lower performance of the Bundler method was due to its poor performance in estimating camera focal length, which, in turn, introduced large errors in the Z-axis for the translation equations.

Subfields: Aerospace Engineering, Geology, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/4adeeb79b828aef5199a576106ce47a6b4d67563

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Citations in the corpus

Extracting leaf area index using viewing geometry effects—A new perspective on high-resolution unmanned aerial system photography (2018, background): “592 Increasing availability of functional-structural plant models (Vos et al., 2010) and 3-D radiative transfer 593 models (e.g. DART, CESBIO 2017) may provide the underlying tools to facilitate such research. 594 Acknowledgment 595 We would like to acknowledge all people involved in field managemen”
Structure from motion photogrammetry in physical geography (2016, methodology): “Alternatively, ‘direct’ georeferencing and scaling can be performed from known camera positions derived from RTK-GPS measurements and an Inertial Measurement Unit (Tsai et al., 2010; Turner et al., 2014).”
Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance (2015, result): “We used Agisoft Photoscan (Version 1.0.4. http://www.agisoft.com/) since it showed good results in a previous study (Turner et al., 2014).”
Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging (2014, background): “The first studies [52–55] show that this approach is close to operational use [29].”
Time Series Analysis of Landslide Dynamics Using an Unmanned Aerial Vehicle (UAV) (2015, background): “[26], to remove the requirement for Ground Control Points (GCPs).”
Assessment of Forest Structure Using Two UAV Techniques: A Comparison of Airborne Laser Scanning and Structure from Motion (SfM) Point Clouds (2016, methodology): “Detailed descriptions of the Photoscan workflow can be found in [12,13,25].”
Unmanned Aerial Vehicle for Remote Sensing Applications - A Review (2019, background): “With advanced photogrammetric processing pipelines [16,17], 3D (3 dimensional) geometric information such as triangular meshes and DSMs are nowadays becoming standard products for UAV-based RS missions.”