An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV) Imagery, Based on Structure from Motion (SfM) Point Clouds

By Darren Turner, Arko Lucieer, Christopher Stephen Watson. Remote Sensing, 2012

726 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Environmental Engineering, Geology

View Paper: https://www.semanticscholar.org/paper/3c7ea1e4d64977d450a38a7a42c14d4497123f88

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Citations in the corpus

High-Resolution Airborne UAV Imagery to Assess Olive Tree Crown Parameters Using 3D Photo Reconstruction: Application in Breeding Trials (2015, background): “advantages of these approaches lies in their potential for performing accurate 3D imagery reconstructions from datasets with a large number of images and a high overlap percentage, even if accurate internal and external camera orientation parameters are not available [18].”
High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing (2016, methodology): “[34] found GCPs offered a significant improvement in spatial accuracy compared to directly georeferenced imagery using the UAVs on-board GPS.”
Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging (2014, neutral): “01-m resolution *TIF-file (Figure 2; [38,39]).”
Aerial photography collected with a multirotor drone reveals impact of Eurasian beaver reintroduction on ecosystem structure1 (2015, methodology): “2012; Colomina and Molina 2014), which can also yield three-dimensional (3D) models quantifying ecosystem structure, using techniques, such as structure-from-motion (SfM) photogrammetry (Turner et al. 2012; Lucieer et al. 2013).”
Analyzing High Resolution Topography for Advancing the Understanding of Mass and Energy Transfer Through Landscapes: A Review (2015, methodology): “The georefencing operation consists of a Helmert Transformation (7 parameters: 1 scale parameter, 3 translation parameters, and 3 rotation parameters) (Turner et al., 2012).”
Direct Georeferencing of Ultrahigh-Resolution UAV Imagery (2014, background): “The UAV has eight rotors (an OktoKopter) and can lift a payload of up to 2 kg for a flight duration of 5–10 min.”
Predicting cover crop biomass by lightweight UAS-based RGB and NIR photography: an applied photogrammetric approach (2017, methodology): “These degrees of accuracy are in accordance with those indicated by Turner et al. (2012), Bendig et al. (2013) and Sona et al. (2014).”
Agricultural remote sensing big data: Management and applications (2018, background): “The coverage with multiple images, in turn, provides an opportunity to build three dimensional models of the ground surface with structure from motion (SfM) point clouds through stereo vision (Rosnell and Honkavaara 2012; Turner et al. 2012; Mathews and Jensen 2013) for extraction of surface feature”
Multi-Temporal Site-Specific Weed Control of Cirsium arvense (L.) Scop. and Rumex crispus L. in Maize and Sugar Beet Using Unmanned Aerial Vehicle Based Mapping (2018, methodology): “Based on the Structure from Motion technique, 3D Models and Digital Elevation Models (DEM) can be created out of UAV imagery [10,11].”