The impact of number and spatial distribution of GCPs on the positional accuracy of geospatial products derived from low-cost UASs

Rangel, José Manuel Galván; Gonçalves, Gil; Pérez, Juan Antonio

doi:10.1080/01431161.2018.1515508

Cited by 62 publications

(53 citation statements)

References 29 publications

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“…Analysing the Figure 14, it can be seen that, for the ChPs situated on the roof edge and on the building façade, the errors are still larger when compared with the remaining ones, when using 20 GCPs or more. This was also the case of the Rangel et al research [22], where the errors tend to be clustered in areas with abrupt slope changes. They also mentioned that altimetric errors are located in areas with little texture and radiometric uniformity.…”

Section: Resultssupporting

confidence: 58%

“…In the last years, finding the optimum number of ground control points needed for a UAV flight was of prime interest to researchers, but in special for georeferencing the DSM [17][18][19][20][21][22], the orthoimage [17,19,[22][23][24][25] and the point clouds [26,27] generated by processing the UAS images. Studies on the accuracy assessment of Structure from Motion (SfM) image block orientation have also been performed.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, the general conclusion that is stated by Sanz-Ablanedo et al [5] cannot be applied on small projects, where less than 100 images are captured. Rangel et al [22] assessed the accuracy of DSM and orthophoto obtained by the SfM method testing 13 scenarios with 177 GCPs and ChPs. The flights were done over a natural area (open pit mine) at an elevation of 228 m with a rotor UAS.…”

Section: Introductionmentioning

confidence: 99%

“…Regarding the spatial distribution of GCPs, Sanz-Ablanedo et al [5] demonstrated that these should be evenly distributed on the study site, ideally in a triangular node grid, but finding a measure for defining the most suitable geometrical distribution of GCPs was not the subject of the research, with authors affirming that this topic requires further investigation. Rangel et al [22] studied the spatial distribution of GCPs relative to the image block (peripheral and internal), finding that the inclusion of GCPs inside the block does not substantially improve the planimetric error, but, in the case of altimetric errors, as the number of GCPs increases, the error decreases considerably. Moreover, the authors found that, for best results, a uniform distribution of GCPs inside the block with a horizontal separation of three to four ground bases (distance between two camera positions) must be assured.…”

Section: Introductionmentioning

confidence: 99%

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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: Resultssupporting

confidence: 58%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…In order to testing the potential of direct georeferencing with DJI-P4RTK with as few GCPs as possible, projects were created varying the number and the position of GCPs [27][28][29]. In particular, as already pointed out by Taddia et al using RTK camera locations for the same datasets [22] as well as Forlani et al [30] with a fixed wing aircraft, the nadiral dataset necessarily requires the use of at least one GCP for the model to obtain good accuracy.…”

Section: Generation Of Photogrammetric Models and Dtmsmentioning

confidence: 99%

Coastal Mapping Using DJI Phantom 4 RTK in Post-Processing Kinematic Mode

Taddia

Stecchi²,

Pellegrinelli

2020

Drones

118

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Topographic and geomorphological surveys of coastal areas usually require the aerial mapping of long and narrow sections of littoral. The georeferencing of photogrammetric models is generally based on the signalization and survey of Ground Control Points (GCPs), which are very time-consuming tasks. Direct georeferencing with high camera location accuracy due to on-board multi-frequency GNSS receivers can limit the need for GCPs. Recently, DJI has made available the Phantom 4 Real-Time Kinematic (RTK) (DJI-P4RTK), which combines the versatility and the ease of use of previous DJI Phantom models with the advantages of a multi-frequency on-board GNSS receiver. In this paper, we investigated the accuracy of both photogrammetric models and Digital Terrain Models (DTMs) generated in Agisoft Metashape from two different image datasets (nadiral and oblique) acquired by a DJI-P4RTK. Camera locations were computed with the Post-Processing Kinematic (PPK) of the Receiver Independent Exchange Format (RINEX) file recorded by the aircraft during flight missions. A Continuously Operating Reference Station (CORS) located at a 15 km distance from the site was used for this task. The results highlighted that the oblique dataset produced very similar results, with GCPs (3D RMSE = 0.025 m) and without (3D RMSE = 0.028 m), while the nadiral dataset was affected more by the position and number of the GCPs (3D RMSE from 0.034 to 0.075 m). The introduction of a few oblique images into the nadiral dataset without any GCP improved the vertical accuracy of the model (Up RMSE from 0.052 to 0.025 m) and can represent a solution to speed up the image acquisition of nadiral datasets for PPK with the DJI-P4RTK and no GCPs. Moreover, the results of this research are compared to those obtained in RTK mode for the same datasets. The novelty of this research is the combination of a multitude of aspects regarding the DJI Phantom 4 RTK aircraft and the subsequent data processing strategies for assessing the quality of photogrammetric models, DTMs, and cross-section profiles.

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Mitigating systematic error in topographic models for geomorphic change detection: accuracy, precision and considerations beyond off‐nadir imagery

James

Antoniazza

Robson

et al. 2020

Earth Surf Processes Landf

111

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Unmanned aerial vehicles (UAVs) and structure-from-motion photogrammetry enable detailed quantification of geomorphic change. However, rigorous precision-based change detection can be compromised by survey accuracy problems producing systematic topographic error (e.g. 'doming'), with error magnitudes greatly exceeding precision estimates. Here, we assess survey sensitivity to systematic error, directly correcting topographic data so that error magnitudes align more closely with precision estimates. By simulating conventional grid-style photogrammetric aerial surveys, we quantify the underlying relationships between survey accuracy, camera model parameters, camera inclination, tie point matching precision and topographic relief, and demonstrate a relative insensitivity to image overlap. We show that a current doming-mitigation strategy of using a gently inclined (<15°) camera can reduce accuracy by promoting a previously unconsidered correlation between decentring camera lens distortion parameters and the radial terms known to be responsible for systematic topographic error. This issue is particularly relevant for the wide-angle cameras often integrated into current-generation, accessible UAV systems, frequently used in geomorphic research. Such systems usually perform on-board image pre-processing, including applying generic lens distortion corrections, that subsequently alter parameter interrelationships in photogrammetric processing (e.g. partially correcting radial distortion, which increases the relative importance of decentring distortion in output images). Surveys from two proglacial forefields (Arolla region, Switzerland) showed that results from lower-relief topography with a 10°-inclined camera developed vertical systematic doming errors > 0•3 m, representing accuracy issues an order of magnitude greater than precision-based error estimates. For higher-relief topography, and for nadir-imaging surveys of the lower-relief topography, systematic error was < 0•09 m. Modelling and subtracting the systematic error directly from the topographic data successfully reduced error magnitudes to values consistent with twice the estimated precision. Thus, topographic correction can provide a more robust approach to uncertainty-based detection of event-scale geomorphic change than designing surveys with small off-nadir camera inclinations and, furthermore, can substantially reduce ground control requirements.

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The impact of number and spatial distribution of GCPs on the positional accuracy of geospatial products derived from low-cost UASs

Cited by 62 publications

References 29 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

Coastal Mapping Using DJI Phantom 4 RTK in Post-Processing Kinematic Mode

Mitigating systematic error in topographic models for geomorphic change detection: accuracy, precision and considerations beyond off‐nadir imagery

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