Determining the Optimum Number of Ground Control Points for Obtaining High Precision Results Based on UAS Images

Oniga, Valeria-Ersilia; Breaban, Ana-Ioana; Stătescu, Florian

doi:10.3390/ecrs-2-05165

Cited by 73 publications

(59 citation statements)

References 7 publications

(12 reference statements)

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“…Analysing the results reported in [32], we can see that the differences between the RMSE that were obtained for each number of GCPs by processing the projects with Pix4D software and Zephyr Pro software [29], are similar with a range of a few millimetres up to 3 cm, except for the first three case: using three, four, and five GCPs. Moreover, when using the 3DF Zephyr Pro software, all images were orientated without making any additional steps.…”

Section: Methodsmentioning

confidence: 99%

Determining the Suitable Number of Ground Control Points for UAS Images Georeferencing by Varying Number and Spatial Distribution

et al. 2020

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Currently, products that are obtained by Unmanned Aerial Systems (UAS) image processing based on structure-from-motion photogrammetry (SfM) are being investigated for use in high precision projects. Independent of the georeferencing process being done directly or indirectly, Ground Control Points (GCPs) are needed to increase the accuracy of the obtained products. A minimum of three GCPs is required to bring the results into a desired coordinate system through the indirect georeferencing process, but it is well known that increasing the number of GCPs will lead to a higher accuracy of the final results. The aim of this study is to find the suitable number of GCPs to derive high precision results and what is the effect of GCPs systematic or stratified random distribution on the accuracy of the georeferencing process and the final products, respectively. The case study involves an urban area of about 1 ha that was photographed with a low-cost UAS, namely, the DJI Phantom 3 Standard, at 28 m above ground. The camera was oriented in a nadiral position and 300 points were measured using a total station in a local coordinate system. The UAS images were processed using the 3DF Zephyr software performing a full BBA with a variable number of GCPs i.e., from four up to 150, while the number and the spatial location of check points (ChPs) was kept constant i.e., 150 for each independent distribution. In addition, the systematic and stratified random distribution of GCPs and ChPs spatial positions was analysed. Furthermore, the point clouds and the mesh surfaces that were automatically derived were compared with a terrestrial laser scanner (TLS) point cloud while also considering three test areas: two inside the area defined by GCPs and one outside the area. The results expressed a clear overview of the number of GCPs needed for the indirect georeferencing process with minimum influence on the final results. The RMSE can be reduced down to 50% when switching from four to 20 GCPs, whereas a higher number of GCPs only slightly improves the results.

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Section: Methodsmentioning

confidence: 99%

Determining the Suitable Number of Ground Control Points for UAS Images Georeferencing by Varying Number and Spatial Distribution

et al. 2020

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“…The horizontal and vertical accuracy reached was 0.20 cm and 3.5 cm, respectively, which is higher than what is allowed for many engineering applications, particularly in the vertical component (e.g., civil and surveying engineering). These limitations are similar for several other studies, where they solely compare the accuracy between a limited number of checkpoints and not the overall 3D model within the area mapped [25,26,[33][34][35][36][37][38][39][40][41][42][43]. The results achieved in these studies are highly variable requiring the need for thorough evaluations comprised of a greater sample.…”

Section: Introductionmentioning

confidence: 55%

Comparing sUAS Photogrammetrically-Derived Point Clouds with GNSS Measurements and Terrestrial Laser Scanning for Topographic Mapping

Mora

Suleiman

Chen

et al. 2019

Drones

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Interest in small unmanned aircraft systems (sUAS) for topographic mapping has significantly grown in recent years, driven in part by technological advancements that have made it possible to survey small- to medium-sized areas quickly and at low cost using sUAS aerial photography and digital photogrammetry. Although this approach can produce dense point clouds of topographic measurements, they have not been tested extensively to provide insights on accuracy levels for topographic mapping. This case study examines the accuracy of a sUAS-derived point cloud of a parking lot located at the Citizens Bank Arena (CBA) in Ontario, California, by comparing it to ground control points (GCPs) measured using global navigation satellite system (GNSS) data corrected with real-time kinematic (RTK) and to data from a terrestrial laser scanning (TLS) survey. We intentionally chose a flat surface due to the prevalence of flat scenes in sUAS mapping and the challenges they pose for accurately deriving vertical measurements. When the GNSS-RTK survey was compared to the sUAS point cloud, the residuals were found to be on average 18 mm and −20 mm for the horizontal and vertical components. Furthermore, when the sUAS point cloud was compared to the TLS point cloud, the average difference observed in the vertical component was 2 mm with a standard deviation of 31 mm. These results indicate that sUAS imagery can produce point clouds comparable to traditional topographic mapping methods and support other studies showing that sUAS photogrammetry provides a cost-effective, safe, efficient, and accurate solution for topographic mapping.

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“…Here, it is demonstrated that an automated method that relies on the RGB reference accuracy, requires just a few well-distributed GCPs (minimum five), high-precision GNSS base stations, and GNSS/IMU sensors integrated with the cameras, to produce high-quality results. Moreover, recent studies [76] have found that a minimum of three GCP/ha are sufficient to assure sub centimeter-level horizontal accuracies when operating similar UAV-based RGB systems at 30 m above the ground approximately. One of our study cases reached absolute accuracies of~1.5 pixels for RGB orthophotos with 5 mm GSD, and relative accuracies between two and seven pixels for hyperspectral images with millimetric resolution (7 mm).…”

Section: Discussionmentioning

confidence: 99%

Automated Georectification and Mosaicking of UAV-Based Hyperspectral Imagery from Push-Broom Sensors

et al. 2019

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Hyperspectral systems integrated on unmanned aerial vehicles (UAV) provide unique opportunities to conduct high-resolution multitemporal spectral analysis for diverse applications. However, additional time-consuming rectification efforts in postprocessing are routinely required, since geometric distortions can be introduced due to UAV movements during flight, even if navigation/motion sensors are used to track the position of each scan. Part of the challenge in obtaining high-quality imagery relates to the lack of a fast processing workflow that can retrieve geometrically accurate mosaics while optimizing the ground data collection efforts. To address this problem, we explored a computationally robust automated georectification and mosaicking methodology. It operates effectively in a parallel computing environment and evaluates results against a number of high-spatial-resolution datasets (mm to cm resolution) collected using a push-broom sensor and an associated RGB frame-based camera. The methodology estimates the luminance of the hyperspectral swaths and coregisters these against a luminance RGB-based orthophoto. The procedure includes an improved coregistration strategy by integrating the Speeded-Up Robust Features (SURF) algorithm, with the Maximum Likelihood Estimator Sample Consensus (MLESAC) approach. SURF identifies common features between each swath and the RGB-orthomosaic, while MLESAC fits the best geometric transformation model to the retrieved matches. Individual scanlines are then geometrically transformed and merged into a single spatially continuous mosaic reaching high positional accuracies only with a few number of ground control points (GCPs). The capacity of the workflow to achieve high spatial accuracy was demonstrated by examining statistical metrics such as RMSE, MAE, and the relative positional accuracy at 95% confidence level. Comparison against a user-generated georectification demonstrates that the automated approach speeds up the coregistration process by 85%.Remote Sens. 2020, 12, 34 2 of 25 understanding [2]. One of the key constraints in our observation capacity relates to the compromise between spatial and temporal resolution, i.e., space-based platforms tend to suffer from either spatial or temporal restrictions that affect the frequency and fidelity of retrievals. Within the last decade, developments in remote sensing using unmanned aerial vehicles (UAV) have provided a sensor-flexible platform that has lowered operational costs, while providing unprecedented spatial (<10 cm) and on-demand temporal resolution [3,4]. To leverage these technological advances, hyperspectral camera systems capturing radiances across the visible and near-infrared portions of the spectrum [5] have been developed, with diverse applications being presented in agriculture [6][7][8], forestry [9], and mining [10] studies. However, while UAV technology has progressed rapidly and there is a level of maturity in many sensing capabilities [11], routine application of hyperspectral imaging systems remains chall...

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Determining the Optimum Number of Ground Control Points for Obtaining High Precision Results Based on UAS Images

Cited by 73 publications

References 7 publications

Determining the Suitable Number of Ground Control Points for UAS Images Georeferencing by Varying Number and Spatial Distribution

Determining the Suitable Number of Ground Control Points for UAS Images Georeferencing by Varying Number and Spatial Distribution

Comparing sUAS Photogrammetrically-Derived Point Clouds with GNSS Measurements and Terrestrial Laser Scanning for Topographic Mapping

Automated Georectification and Mosaicking of UAV-Based Hyperspectral Imagery from Push-Broom Sensors

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