Number Of Ground Control Points Research Articles

A New Precise Point Positioning with Ambiguity Resolution (PPP-AR) Approach for Ground Control Point Positioning for Photogrammetric Generation with Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) are now widely preferred systems that are capable of rapid mapping and generating topographic models with relatively high positional accuracy. Since the integrated GNSS receivers of UAVs do not allow for sufficiently accurate outcomes either horizontally or vertically, a conventional method is to use ground control points (GCPs) to perform bundle block adjustment (BBA) of the outcomes. Since the number of GCPs to be installed limits the process in UAV operations, there is an important research question whether the precise point positioning (PPP) method can be an alternative when the real-time kinematic (RTK), network RTK, and post-process kinematic (PPK) techniques cannot be used to measure GCPs. This study introduces a novel approach using precise point positioning with ambiguity resolution (PPP-AR) for ground control point (GCP) positioning in UAV photogrammetry. For this purpose, the results are evaluated by comparing the horizontal and vertical coordinates obtained from the 24 h GNSS sessions of six calibration pillars in the field and the horizontal length differences obtained by electronic distance measurement (EDM). Bartlett’s test is applied to statistically determine the accuracy of the results. The results indicate that the coordinates obtained from a two-hour PPP-AR session show no significant difference from those acquired in a 30 min session, demonstrating PPP-AR to be a viable alternative for GCP positioning. Therefore, the PPP technique can be used for the BBA of GCPs to be established for UAVs in large-scale map generation. However, the number of GCPs to be selected should be four or more, which should be homogeneously distributed over the study area.

GCP Placement Methods for Improving the Accuracy of Shoreline Extraction in Coastal Video Monitoring

In coastal video monitoring, the direct linear transform (DLT) method with ground control points (GCPs) is commonly used for geo-rectification. However, current practices often overlook the impact of GCP quantity, arrangement, and the geographical characteristics of beaches. To address this, we designed scenarios at Chuam Beach to evaluate how factors such as the distance from the camera to GCPs, the number of GCPs, and the height of each point affect the DLT method. Accuracy was assessed by calculating the root mean square error of the distance errors between the actual GCP coordinates and the image coordinates for each setting. This analysis aims to propose an optimal GCP placement method. Our results show that placing GCPs within 200 m of the camera ensures high accuracy with few points, whereas positioning them at strategic heights enhances shoreline extraction. However, since only fixed cameras were used in this study, factors like varying heights, orientations, and resolutions could not be considered. Based on data from a single location, we propose an optimal method for GCP placement that takes into account distance, number, and height using the DLT method.

Solutions to enhance the accuracy of UAV’s images for creating extremely large-scale maps in urban area

Abstract Ho Chi Minh City, the largest urban agglomeration in Vietnam, is at the forefront of enabling the government’s efforts and advocating for smart city initiatives in the coming decades. To facilitate this advancement, the implementation of solutions to support the collection, construction, and updating of spatial databases is crucial. These databases serve as the foundational platform for effective urban management practices. Unmanned Aerial Vehicles (UAVs) have witnessed significant growth across various domains, particularly in spatial data collection. However, challenges arise when acquiring and processing UAV images in urban areas. These challenges include orthomosaic distortion in certain regions, insufficient accuracy for creating extremely large-scale maps (e.g., 1:200 or 1:500), and unclear boundaries between adjacent objects. The objective of this paper is to present flight tests conducted at varying altitudes, with different shooting angles, and varying layouts of Ground Control Points (GCPs), both with and without the UAV’s RTK receiver mode. In order to improve the accuracy of UAV’s images, the results suggest that the precision of orthomosaic, Digital Surface Model (DSM) and point clouds in the context of 3D mapping depends on various factors, including: the determination of the optimal flight altitude, optimizing number of GCPs, selecting an appropriate level of image processing, capturing images from varied angles, and using UAVs with RTK or PPK data acquisition modes to mitigate image distortion.

Accuracy analysis of UAV aerial photogrammetry based on RTK mode, flight altitude, and number of GCPs

Abstract The optimization of an unmanned aerial vehicle (UAV) aerial photogrammetry scheme is crucial for achieving higher precision mapping results. Three representative factors, namely the real-time kinematic (RTK) mode, flight altitude, and the number of ground control points (GCPs) were selected to analyze their impact on UAV aerial photogrammetry accuracy. Four flight altitude tests were conducted separately in two RTK modes, and five GCP layout schemes were designed. Based on this, the root mean square error (RMSE) values of 40 aerial photogrammetric results were analyzed. The results showed a significant correlation between flight altitude and resolution of the UAV aerial photogrammetric results. Further, conversion formulas between actual image resolution and flight altitude for different GCP values were also derived in RTK and non-RTK modes. In the case of precise positioning, the horizontal and vertical accuracy of the aerial photogrammetric image decreased with increasing flight altitude. Under the same flight altitude, the addition or no addition of GCPs, including changes in GCP numbers, had no significant effect on improving the accuracy of aerial photogrammetry in RTK mode. However, in non-RTK mode, the number of GCPs significantly affected accuracy. The horizontal and vertical RMSE values decreased rapidly with the increase in GCP numbers and then stabilized. However, regardless of whether RTK was activated, an excessive number of GCPs was not conducive to improving the accuracy of aerial photogrammetric results. The mapping accuracy of UAVs in RTK mode without GCPs was equivalent to that in non-RTK mode with GCPs. Therefore, when using RTK-UAVs, deploying GCPs is unnecessary under suitable circumstances. Finally, practical suggestions for optimizing the UAV aerial photogrammetry scheme are provided as a reference for related applications.

Updating Orthophotos around Gas Pipelines based on "Cloud Control" Photogrammetry

Abstract. As a clean energy source, natural gas is widely used and primarily transported through long-distance pipelines. Regular maintenance and inspection of long-distance gas pipelines are crucial tasks. Due to the extensive coverage and distance of these pipelines, the workload is enormous. It is necessary to first identify areas of change, which can be carried out using multiple sets of orthophotos produced by unmanned aerial vehicles (UAVs). However, UAV images have small footprints and significant geometric distortions, requiring a large number of ground control points (GCPs) for accurate positioning. Measuring these points in the field is challenging and time-consuming, becoming a key factor limiting the rapid production of orthophotos. To overcome this challenge, this paper introduces the "cloud control" photogrammetry technology to achieve fully automatic updates of orthophotos around long-distance pipelines, providing foundational data for the maintenance and inspection of these gas pipelines. This method replaces GCPs with images containing known orientation parameters, serving as control information. By matching tie points between new and old images, the "cloud control points" are transferred to the new images, enabling the image registration and production of orthophotos. The experiments conducted on the Fumin and Zhaotong segments of a long-distance gas pipeline in Yunnan Province demonstrate that, for UAV images with a ground resolution of 0.05 meters, using the "cloud control" method achieves a planar accuracy of 0.05 meters and an elevation accuracy of 0.07 meters. These results are comparable to the accuracy obtained by orienting the results using GCPs.

The Importance of Ground Control Points In A Photogrammetric Workflow

Abstract This study investigates the optimal distribution and pattern of ground control points (GCPs) in aerial photogrammetric projects. Aerial triangulation (AT), also known as bundle adjustment, is the fundamental step in refining 3D reconstruction models and camera positions, thereby minimizing reprojection errors. The study utilizes data from a national project in Romania, employing high-resolution aerial images acquisition using photogrammetric sensors. The project has rigorous requirements of ground control points (GCP) placement and field measurements using GNSS and geometric leveling techniques. The study employs various scenarios, manipulating the number and distribution of GCPs, to assess their influence on planimetric and altimetric accuracy. Results indicate that the configuration and number of GCPs significantly affect the accuracy of photogrammetric products, such as dense image point clouds, digital surface models, and orthophotos. Moreover, the study underscores the importance of precise GCP determination methods, especially in regions lacking a precise gravimetric geoid model. In scenarios with inadequate GCP coverage the outcomes have inferior quality, emphasizing the critical role of GCPs in ensuring the quality of photogrammetric products. Overall, the research gives a clear view on the best placement patterns of GCPs and their influence on AT process evaluation performed in check points (CHKs).

Optimization of Number of GCPs and Placement Strategy for UAV-Based Orthophoto Production

Unmanned aerial vehicles (UAVs) have been employed to perform aerial surveys in many industries owing to their versatility, relatively low cost, and efficiency. Ground control points (GCPs) are used for georeferencing to ensure orthophoto geolocation/positioning accuracy. In this study, we investigate the impact of the number and distribution of GCPs on the accuracy of orthophoto production based on images acquired by UAVs. A test site was selected based on regulatory requirements, and several scenarios were developed considering the specifications of the UAVs used in this study. The locations of GCPs were varied to obtain the results. Based on the results obtained for different numbers of GCPs per unit area and distribution of GCPs, it is shown that UAV-based platforms can be more extensively utilized in a range of applications. The findings of this study will significantly impact the development process of GCP automation algorithms and enable a more cost-effective approach when determining target sites for UAV-based orthophoto production.

Optimal camera focal length detection method for GPS-supported bundle adjustment in UAV photogrammetry

The global positioning system (GPS) supported bundle adjustment (BA) method of unmanned aerial vehicle (UAV) photogrammetry significantly minimises the number of ground control points (GCPs); however, there are often systematic errors in the vertical axis direction of the 3D reconstruction results. In this study, a method that relies only on a small number of GCPs for detecting and estimating the optimal focal length of the camera is proposed. First, the scene structure information is obtained based on the conventional GPS-supported BA optimisation method, and the camera focal length is redefined based on a single GCP. Second, a second-order polynomial is used to fit the redefined camera focal lengths of multiple GCPs and the corresponding positioning accuracy. The highest accuracy position is selected as the optimal focal length of the camera. Finally, the optimal focal length of the camera is taken as a constant, and the conventional GPS-supported BA method is used to complete the optimisation again. The experimental results show that under the same number and distribution of GCPs, the camera detection method’s total positioning accuracy proposed in this paper is improved by an average of 37.64%. The optimal camera focal length obtained by the method can correct the systematic error in the vertical direction while keeping the vertical accuracy consistent with the horizontal accuracy.

The impact of ground control points for the 3D study of grapevines in steep slope vineyards

Data acquisition through unmanned aerial vehicles (UAVs) has become integral to the study of agricultural crops, especially for multitemporal analyses spanning the entire growing season. Ensuring accurate data alignment is essential not only to maintain data quality but also to leverage the continuous monitoring of the same area over time. Ground control points (GCPs) play a critical role in geolocating UAV data. Their absence can lead to planimetric and altimetric discrepancies, which are particularly impactful in 3D plant-level studies. This study is centered on the examination of misalignment effects in a challenging steep slope vineyard environment and their impacts on 3D alignment accuracy. For this purpose, a UAV equipped with an RGB camera to capture imagery at two distinct flight heights. Various scenarios, each involving a different number of GCPs, were assessed to evaluate their impact on alignment precision. The methodology employed holds potential for assessing geolocation accuracy in complex 3D environments, providing value insights for vineyard monitoring.

Improving UAV‐SfM photogrammetry for modelling high‐relief terrain: Image collection strategies and ground control quantity

AbstractImage collection strategies and ground control points (GCPs) are of particular importance for uncrewed aerial vehicle combined with Structure‐from‐Motion (UAV–SfM) photogrammetry, and the generalization of their effects has proved elusive. This study designed various photogrammetric scenarios to investigate the effects of image collection strategies, ground control quantity, and their interaction on digital elevation model (DEM) errors and their spatial structure in high‐relief terrain. The results of 1.77 × 105 UAV–SfM scenarios provide insights for improving UAV–SfM practices. A high image capture angle (20–40°) enhances camera calibration quality decreasing the magnitude and spatial correlation of errors. High camera inclination reduces the sensitivity of mean and standard deviation of error to flying height but not the spatial correlation of error. Including additional data (e.g. supplemented convergent images; images captured at multiple flying heights) has only a minor effect if imagery is highly inclined. GCPs provide more effective constraints than image collection strategies. The mean error and standard error decline quickly with a small number of GCPs and then become stable in all scenarios, but the spatial correlation of error can be further improved with increasing GCPs. However, the effects of GCP quantity do interact with image collection strategies. High camera inclination reduces requirements for GCPs, whilst strategies combining different flying heights and image orientations have little effect on necessary GCP quantity. The distribution of GCPs still affects the errors, but the effect of GCP distribution becomes less important with an increase in the number of GCPs. Finally, we show that UAV–SfM photogrammetric quality assessment should routinely assess the spatial dependence of error using a statistic like Moran's I.

INVESTIGATION OF THE ACCURACY OF DETERMINING PLANNED AND ALTITUDE COORDINATES BASED ON SURVEY DATA BY UNMANNED AERIAL VEHICLES

Experimental studies of the accuracy of determining the plan and altitude coordinates were obtained from aerial photography materials and measured by a satellite receiver in real time. The study was conducted for aerial photography materials obtained from heights of 100 and 200 meters. In addition, the accuracy researched of using a different number of ground control points for georeferencing images – 18 and 5. Accuracy assessment was performed separately for the plain area and the area with rugged terrain.

Feasibility of Accurate Point Cloud Model Reconstruction for Earthquake-Damaged Structures Using UAV-Based Photogrammetry

Camera-enabled unmanned aerial vehicles (UAVs) provide a promising technique to considerably speed up the inspection and visual data collection from regions that may otherwise be inaccessible. In addition, the technology of image-based 3D reconstruction can generate a point cloud model using images captured by UAVs. However, the performance of the point cloud modeling may be affected by multiple factors, such as the modeling software, ground control points (GCPs), and UAV flight modes. In this study, three common software packages were compared, and Pix4Dmapper was considered a suitable software for point cloud modeling for earthquake-damaged buildings. The accuracy and resolution of point cloud models are usually evaluated by root mean square error (RMSE) and ground sampling distance (GSD). The effects of the main factors, including the number of GCPs, distribution of GCPs, flight manner of the UAV, and distance from the UAV to the target, were investigated on the basis of two real-world multistory earthquake-damaged structures. The influence rules of the main factors revealed that a close range, automatic flight mode of the UAV, a large number of GCPs, and a relatively wide distribution of the GCPs may generate a point cloud model with low computational costs, high accuracy, and high resolution. In the particular illustration example here, the RMSE is 6.78 mm while the GSD is 1.60 mm. Finally, rapid structural damage inspection was demonstrated using an accurate point cloud model and compared with the inspection results of a total station and terrestrial laser scanner point cloud models. The comparison of different inspection results showed that the relative errors were relatively acceptable within 4%.

ASSESSING THE VIABILITY OF PPK TECHNIQUES FOR ACCURATE MAPPING WITH UAS

Abstract. Utilizing ground control points (GCPs) to georeference photogrammetry-based point cloud data is a common practice in unmanned aerial system (UAS) mapping. Direct georeferencing or integrated sensor orientation (ISO) can be used to obtain georeferenced point clouds from UAS without relying heavily on GCPs. However, the accuracy of the point cloud may be impacted by the accuracy of the trajectory solution obtained by GNSS. To improve point cloud accuracy, post-processing kinematic (PPK) solutions can be applied to the UAS trajectory, which may provide higher accuracy than low-accuracy trajectory solutions and minimize the reliance on GCPs. This study compares the accuracy and precision of two different point clouds generated using different methods. One point cloud was generated using traditional photogrammetric methods with low accuracy Global Navigation Satellite System (GNSS) observations from the UAS and GCPs that have an average accuracy of one to two centimeters, while the other was generated using PPK trajectory solution for the UAS’s trajectory with two software: open-source Emlid Studio and the widely used Inertial Explorer. The use of PPK techniques in UAS mapping may have several potential benefits over traditional methods. By correcting the errors in the UAS's trajectory, a user may only need to depend on fewer ground control points, which can reduce the time and cost associated with fieldwork. This is particularly useful in areas that are difficult to access or have limited ground control point options, such as in urban or forested areas. To evaluate performance, a GNSS receiver is used to obtain measurements on checkpoints, which are used to assess the accuracies of the point clouds. In our experiments, the accuracy of the point clouds generated using PPK trajectory solution with high accuracy GCPs was found to be higher than those generated with low accuracy GNSS observations while aided with high accuracy ground control points. While the use of PPK with GCPs is generally expected to provide more accurate and reliable data than low-accuracy GNSS observations even after adjusting with GCPs, the number and distribution of GCPs can still significantly impact overall accuracy. Therefore, careful consideration of the number of GCPs and their placement is essential to achieve the desired level of efficiency and effectiveness in UAS mapping.

A Robust Method for Block Adjustment of UAV SAR Images

To reduce the number of ground control points required for block adjustment of unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) images, a robust block adjustment method is pro-posed. The proposed method aims to solve the problem of unstable adjustment solutions caused by the jittering of UAV SAR platforms. This method is based on the range Doppler model. By analyzing the correlation between the antenna phase center, velocity, and Doppler centroid of the seven orientation parameters, this method performs a block adjustment of UAV SAR images with three orientation parameters: the antenna phase center initial imaging moment, Doppler centroid, and proximity delay. UAV SAR images obtained from an area of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> 3 km in Dengfeng were used to conduct adjustment experiments. The results obtained using different orientation parameter set-tings verify the robustness and effectiveness of the proposed method. The adjustment results under various control-point layout plans indicate that a uniform layout plan can achieve an adjustment accuracy better than 1 m with a small number of control points.

Accuracy analysis of UAV photogrammetry using RGB and multispectral sensors

In recent years, unmanned aerial vehicles (UAVs) have become increasingly important as a tool for quickly collecting high-resolution (spatial and spectral) imagery of the Earth's surface. The final products are highly dependent on the choice of values for various parameters in flight planning, the type of sensors, and the processing of the data. In this paper ground control points (GCPs) were first measured using the Global Navigation Satellite System (GNSS) Real-Time Kinematic (RTK) method, and then due to the low height accuracy of the GNSS RTK method all points were measured using a detailed leveling method. This study aims to provide a basic assessment of quality, including four main aspects: (1) the difference between an RGB sensor and a five-band multispectral sensor on accuracy and the amount of data, (2) the impact of the number of GCPs on the accuracy of the final products, (3) the impact of different altitudes and cross flight strips, and (4) the accuracy analysis of multi-altitude models. The results suggest that the type of sensor, flight configuration, and GCP setup strongly affect the quality and quantity of the final product data while creating a multi-altitude model does not result in the expected quality of data. With its unique combination of sensors and parameters, the results and recommendations presented in this paper can assist professionals and researchers in their future work.

An Automatic Geometric Registration Method for Multi Temporal 3D Models

The application research of ground change detection based on multi-temporal 3D models is attracting more and more attention. However, the conventional methods of using UAV GPS-supported bundle adjustment or measuring ground control points before each data collection are not only economically costly, but also have insufficient geometric accuracy. In this paper, an automatic geometric-registration method for multi-temporal 3D models is proposed. First, feature points are extracted from the highest resolution texture image of the 3D model, and their corresponding spatial location information is obtained based on the triangular mesh of the 3D model, which is then converted into 3D spatial-feature points. Second, the transformation model parameters of the 3D model to be registered relative to the base 3D model are estimated by the spatial-feature points with the outliers removed, and all the vertex positions of the model to be registered are updated to the coordinate system of the base 3D model. The experimental results show that the position measurement error of the ground object is less than 0.01 m for the multi-temporal 3D models obtained by the method of this paper. Since the method does not require the measurement of a large number of ground control points for each data acquisition, its application to long-period, high-precision ground monitoring projects has great economic and geometric accuracy advantages.

Developing a procedure to produce large-scale topographic maps of open-pit mines using rotary wing unmanned aerial vehicles

Recently, the rapid development of unmanned aerial vehicle (UAV) has led to increasingly popular applications of this technology in the field of surveying and mapping. This article presents the result of developing a procedure to produce large-scale topographic maps for open-pit mines in Vietnam in which unmanned aerial vehicles (UAV) are used to collect data. With the goal of creating a procedure that ensures accuracy, safety, and efficiency for the production of topographic maps, the theories and official regulations for measuring and editing topographic maps in general and in mines in particular, as well as using drones in Vietnam have been applied. The procedure is specifically developed for the vertical takeoff and landing (rotary) UAVs. Experiments consisting of the flight altitude calculation, determining the appropriate take-off position, and image acquisition were conducted at two open-pit mines, namely Dong Da Mai coal mine and Long Son quarry to verify the procedure. The digital surface models (DSM) and orthophotos are generated from the acquired photos, georeferenced to the VN-2000 coordinate system using ground control points (GCP), and evaluated with checkpoints. In addition, the experiment included the investigation of the various combination of GCP networks to determine the optimal GCP number for each case. Experimental results show that the procedure is logical, correct, straightforward, safe, and effective. UAV resulted products, such as orthophotos, DSM, and topographic maps were accurate and met the requirements of all the national standards.

KOREKSI GEOMETRIK DATA DRONE DENGAN METODE AFFINE DAN METODE POLYNOMIAL ORDE 2 PADA PEMETAAN LAHAN SAWAH DI BANJAR TEGAL, DESA SANGSIT

The need for geospatial data as geographic information of an area needs an update because every year the development of an area is increasing. The use of UAV or drone technology can help speed up various jobs, one of which is obtaining geospatial data. The use of drones in mapping of course regarding the accuracy of most positions is still low. So it is necessary to make geometric corrections. Geometric correction is carried out with the aim of correcting distortions in the image to match the coordinates of the actual position on the earth's surface. The purpose of this study was to determine the optimal number of GCP in the drone geometric data correction process and to determine the orthorectification mechanism in the drone geometric data correction process.The method used in this research is using the orthorectification method by combining GCP (Ground Control Point) and drone data using ArcGIS software. The determination of the number of GCPs is based on the Affine transformation method with a total of 4 GCP points and an Order-2 Polynomial transformation method with a total of 8 GCP points. The results of this study are visualization of drone data that has been rectified with a total horizontal geometric accuracy of 0,160129635 m on the Affine method and 0,06590305225 m of the second-order polynomial method. So that these two transformation methods meet the requirements for horizontal geometric accuracy on a class 1 map on a 1:1000 scale with an accuracy value of 0.2 m.&#x0D;

MULTIMODE SATELLITE IMAGE HYBRID BLOCK-ADJUSTMENT AND ITS APPLICATION IN LARGE AREA ORTHOPHOTO IMAGE PROCESSING

Abstract. Different satellite images have different positioning accuracy. For example, stereo satellite images have higher positioning accuracy than resource survey satellite images. In addition, for a large number of non stereo satellite images, due to the inability to build a strong triangulation model, it is impossible to carry out block adjustment alone to improve the image positioning accuracy. High precision and high resolution Orthophoto Images are the basis of resource investigation and monitoring and basic geographic information updating. For example, China's third national land survey, national geographic situation monitoring and other national projects require that the survey base map must reach the accuracy of 1:10000 scale, that is, the mean square plane error of points is less than 5m. For most satellite images, a certain number of ground control points need to be deployed to achieve this accuracy. Due to the difficulty of obtaining high-precision ground control points and DEM data in difficult areas, high-precision mapping has always been an unsolved problem, such as Western China. In addition, due to the limited coverage of a single satellite image, to realize the complete coverage of an image in a large area requires the joint application of multiple satellite images. In this paper, the high-precision collaborative geometric processing model and technical method of high-resolution multi-source remote sensing satellite images are proposed. The high-precision collaborative geometric processing of more than ten kinds of high-resolution domestic satellite images is completed by integrating multi-source observation data. An automatic construction method of large-scale block adjustment model of remote sensing images from domestic satellites based on multivariate generalized control network is proposed, including key technologies such as automatic optimization of optimal tie points under different modes, automatic matching of multi-node parallelization tie points, multi-level gross error elimination and so on, which realizes the automatic and stable construction of aerial triangulation model. The test shows that the positioning accuracy of satellite images is better than the accuracy requirements of 1:10000 scale without ground control points, which solves the problem of geometric positioning of 1:10000 scale accuracy in areas where it is difficult to obtain ground control points in the field of Western China.

Improvement of VHR Satellite Image Geometry with High Resolution Elevation Models

The number of high and very high resolution (VHR) optical satellite sensors, as well as the number of medium resolution satellites is continuously growing. However, not all high-resolution optical satellite imaging cameras have a sufficient and stable calibration in time. Due to their high agility in rotation, a quick change in viewing direction can lead to satellite attitude oscillation, causing image distortions and thus affecting image geometry and geo-positioning accuracy. This paper presents an approach based on re-projection of regularly distributed 3D ground points from object in image space, to detect and estimate the periodic distortions of Pléiades tri-stereo imagery caused by satellite attitude oscillations. For this, a hilly region was selected as a test site. Consequently, we describe a complete processing pipeline for computing the systematic height errors (deformations, waves) of the satellite-based digital elevation model by using a Lidar high resolution terrain model. Ground points with fixed positions, but with two elevations (actual and corrected) are then re-projected to the satellite images with the aid of the Rational Polynomial Coefficients (RPCs) provided with the imagery. Therefore, image corrections (displacements) are determined by computing the differences between the distinct positions of corresponding points in image space. Our experimental results in Allentsteig (Lower Austria) show that the systematic height errors of satellite-based elevation models cannot be compensated with an usual or even high number of Ground Control Points (GCPs) for RPC bias correction, due to insufficiently known image orientations. In comparison to a reference Lidar Digital Terrain Model (DTM), the computed elevation models show undulation effects with a maximum height difference of 0.88 m in along-track direction. With the proposed method, image distortions in-track direction with amplitudes of less than 0.15 pixels were detected. After applying the periodic distortion compensation to all three images, the systematic elevation discrepancies from the derived elevation models were successfully removed and the overall accuracy in open areas improved by 33% in the RMSE. Additionally, we show that a coarser resolution reference elevation model (AW3D30) is not feasible for improving the geometry of the Pléiades tri-stereo satellite imagery.