Indirect Georeferencing Research Articles

UAV Data Collection Co-Registration: LiDAR and Photogrammetric Surveys for Coastal Monitoring

When georeferencing is a key point of coastal monitoring, it is crucial to understand how the type of data and object characteristics can affect the result of the registration procedure, and, above all, how to assess the reconstruction accuracy. For this reason, the goal of this work is to evaluate the performance of the iterative closest point (ICP) method for registering point clouds in coastal environments, using a single-epoch and multi-sensor survey of a coastal area (near the Bevano river mouth, Ravenna, Italy). The combination of multiple drone datasets (LiDAR and photogrammetric clouds) is performed via indirect georeferencing, using different executions of the ICP procedure. The ICP algorithm is affected by the differences in the vegetation reconstruction by the two sensors, which may lead to a rotation of the slave cloud. While the dissimilarities between the two clouds can be minimized, reducing their impact, the lack of object distinctiveness, typical of environmental objects, remains a problem that cannot be overcome. This work addresses the use of the ICP method for registering point clouds representative of coastal environments, with some limitations related to the required presence of stable areas between the clouds and the potential errors associated with featureless surfaces.

Comparative Assessment of the Effect of Positioning Techniques and Ground Control Point Distribution Models on the Accuracy of UAV-Based Photogrammetric Production

Unmanned aerial vehicle (UAV) systems have recently become essential for mapping, surveying, and three-dimensional (3D) modeling applications. These systems are capable of providing highly accurate products through integrated advanced technologies, including a digital camera, inertial measurement unit (IMU), and Global Navigation Satellite System (GNSS). UAVs are a cost-effective alternative to traditional aerial photogrammetry, and recent advancements demonstrate their effectiveness in many applications. In UAV-based photogrammetry, ground control points (GCPs) are utilized for georeferencing to enhance positioning precision. The distribution, number, and location of GCPs in the study area play a crucial role in determining the accuracy of photogrammetric products. This research evaluates the accuracy of positioning techniques for image acquisition for photogrammetric production and the effect of GCP distribution models. The camera position was determined using real-time kinematic (RTK), post-processed kinematic (PPK), and precise point positioning-ambiguity resolution (PPP-AR) techniques. In the criteria for determining the GCPs, six models were established within the İstanbul Technical University, Ayazaga Campus. To assess the accuracy of the points in these models, the horizontal, vertical, and 3D root mean square error (RMSE) values were calculated, holding the test points stationary in place. In the study, 2.5 cm horizontal RMSE and 3.0 cm vertical RMSE were obtained with the model containing five homogeneous GCPs by the indirect georeferencing method. The highest RMSE values of all three components in RTK, PPK, and PPP-AR methods were obtained without GCPs. For all six models, all techniques have an error value of sub-decimeter. The PPP-AR technique yields error values that are comparable to those of the other techniques. The PPP-AR appears to be an alternative to RTK and PPK, which usually require infrastructure, labor, and higher costs.

Empirical Evaluation and Simulation of the Impact of Global Navigation Satellite System Solutions on Uncrewed Aircraft System–Structure from Motion for Shoreline Mapping and Charting

Uncrewed aircraft systems (UASs) and structure-from-motion/multi-view stereo (SfM/MVS) photogrammetry are efficient methods for mapping terrain at local geographic scales. Traditionally, indirect georeferencing using ground control points (GCPs) is used to georeference the UAS image locations before further processing in SfM software. However, this is a tedious practice and unsuitable for surveying remote or inaccessible areas. Direct georeferencing is a plausible alternative that requires no GCPs. It relies on global navigation satellite system (GNSS) technology to georeference the UAS image locations. This research combined field experiments and simulation to investigate GNSS-based post-processed kinematic (PPK) as a means to eliminate or reduce reliance on GCPs for shoreline mapping and charting. The study also conducted a brief comparison of real-time network (RTN) and precise point positioning (PPP) performances for the same purpose. Ancillary experiments evaluated the effects of PPK base station distance and GNSS sample rate on the accuracy of derived 3D point clouds and digital elevation models (DEMs). Vertical root mean square errors (RMSEz), scaled to the 95% confidence interval using an assumption of normally-distributed errors, were desired to be within 0.5 m to satisfy National Oceanic and Atmospheric Administration (NOAA) requirements for nautical charting. Simulations used a Monte Carlo approach and empirical tests to examine the influence of GNSS performance on the quality of derived 3D point clouds. RTN and PPK results consistently yielded RMSEz values within 10 cm, thus satisfying NOAA requirements for nautical charting. PPP did not meet the accuracy requirements but showed promising results that prompt further investigation. PPK experiments using higher GNSS sample rates did not always provide the best accuracies. GNSS performance and model accuracies were enhanced when using base stations located within 30 km of the survey site. Results without using GCPs observed a direct relationship between point cloud accuracy and GNSS performance, with R2 values reaching up to 0.97.

Drones and Real-Time Kinematic Base Station Integration for Documenting Inaccessible Ruins: A Case Study Approach

Ruins, marked by decay and abandonment, present challenges for digital documentation due to their varied conditions and remote locations. Surveying inaccessible ruins demands innovative approaches for safety and accuracy. Drones with high-resolution cameras enable the detailed aerial inspection and imaging of these inaccessible areas. This study investigated how surveying technologies, particularly Unmanned Aerial Vehicles (UAVs), are used to document inaccessible ruins. Integration with Real-Time Kinematic (RTK) technologies allows direct georeferencing in photogrammetric processing. A case study of the Castle of Terracorpo in Italy was used to demonstrate UAV-only surveying feasibility in inaccessible environments, testing two different scenarios. The first scenario involved the use of a DJI Matrice 300 RTK coupled with the D-RTK2 base station to survey the Castle; both direct and indirect georeferencing were exploited and compared through the photogrammetric process. This first scenario confirmed that this approach can lead to a centimetre-level accuracy (about three times the GSD value for indirect georeferencing and seven times the GSD value for direct georeferencing exploting RTK). The second scenario testing the integration of data from drones at varying resolutions enabled the comprehensive coverage of ruinous structures. In this case, the photogrammetric survey performed with the dji Mavic 3 Cine drone (indirect georeferencing) was integrated with the photogrammetric survey performed with the dji Matrice 300 RTK drone (direct georeferencing). This scenario showed that GCPs extracted from a direct georeferencing photogrammetric survey could be successfully used to georeference and integrate other drone data. However, challenges persist in surveying underground or enclosed spaces that are inaccessible to UAVs. Future research will explore integrating robotic LiDAR survey systems and advanced data fusion techniques to enhance documentation.

Comparative performance analysis of precise point positioning technique in the UAV − based mapping

Uncrewed Aerial Vehicle (UAV) photogrammetry is a very efficient, low-cost approach for mapping small or medium-sized local areas. This approach is adequate for achieving the needed accuracy in many applications, and onboard positioning sensors have made this technique more feasible by decreasing the need for ground control stations (GCPs). When real-time positioning strategies are used, photogrammetric mapping can also be performed instantaneously in the field. This is especially crucial in applications like disaster management, where current situation must be detected rapidly. It is critical to choose a georeferencing approach in applications that is appropriate for the physical parameters of the mapping area, the intended use of the photogrammetric mapping, and the needed accuracy. In order to accurately georeference the photogrammetric outputs, this study assesses the real-time and post-processed performances of absolute and relative GNSS positioning approaches. Finally, it compares these strategies in terms of the position accuracy of the created models. In the evaluations, the 3D models of the study area were created utilizing network-based Real Time Kinematic (n-RTK), Post-Processed Kinematic (PPK) Positioning, and Precise Point Positioning (PPP) approaches, with direct georeferencing of the UAV-based photogrammetric images. These models were then compared to models developed using traditional indirect georeferencing with GCPs and aided indirect georeferencing. According to the findings, direct georeferencing using the n-RTK and PPK techniques provided a horizontal position accuracy in terms of root mean square errors (rms2D = 1.6 cm and 1.9 cm) almost equal to indirect georeferencing using GCPs (rms2D = 1.9 cm). In terms of vertical position accuracies, the n-RTK and PPK approaches in direct georeferencing (rmsh = 3.7 cm and 3.9 cm, respectively) produced rather different results than the GCPs-based conventional strategy (rmsh = 1.6 cm), although the discrepancies were within acceptable error limits. Direct georeferencing with the PPP approach produced the lowest horizontal and vertical position accuracies (rms2D = 5.9 cm, rmsh= 5.7 cm). However, this level of accuracy was deemed adequate for use in large-scale mapping in Turkey's authorized regulation.

Enhancement of low-cost UAV-based photogrammetric point cloud using MMS point cloud and oblique images for 3D urban reconstruction

The accurate and dense reconstruction of high-quality 3D spatial information is essential for digital twin-based smart cities. Unmanned aerial vehicle (UAV)-based photogrammetry allows the 3D modeling of urban environments in the shortest possible time. Accurate georeferencing is a prerequisite for utilizing geospatial information. The geolocation accuracy of real-time kinematic (RTK)/post-processing kinematic (PPK) UAV-based photogrammetry is significantly high; however, RTK/PPK UAVs are costly. In contrast, the geolocation accuracy of low-cost UAV-based photogrammetry is generally low. It can be improved by indirect georeferencing using ground control points (GCPs) obtained in situ; however, this requires significant amounts of human resources and time. Therefore, this study analyzes the suitability of utilizing the mobile mapping system (MMS) point cloud as GCPs for low-cost UAV-based photogrammetry. We checked the significance of the vertical distribution of GCPs on the geolocation accuracy of low-cost UAV-based photogrammetry using the feature points of building facades (captured via vehicle-based MMS along roads) as GCPs. In addition, typical UAV-based photogrammetry only uses nadir images, which limits the detailed 3D reconstruction of building facades. In this study, a detailed reconstruction and an improved vertical geolocation accuracy were achieved using oblique images. Experiments demonstrated that the geolocation accuracy of the low-cost UAV-based photogrammetric point cloud improved to within 16 cm in the X-, Y-, and Z-directions. It was at its highest when the GCPs were diversely distributed in the vertical direction. Finally, we generated an enhanced point cloud by merging the low-cost UAV-based photogrammetric point cloud and the MMS point cloud.

Developing innovative and cost-effective UAS-PPK module for generating high-accuracy digital surface model

Traditional indirect georeferencing requires time-consuming and labor-intensive field surveys to obtain ground control points (GCPs), making it challenging to apply in high-risk areas with limited accessibility. This study proposes a novel and low-cost system for direct georeferencing using unmanned aerial system post-processing kinematics (UAS-PPK), which is less than a quarter of the price of commercially available products. To evaluate the accuracy of the aerial surveys of the custom-built module and digital surface models, we used 15 checkpoints (CPs) and 99 validation points (VPs). The results showed that this UAS-PPK module could deliver high-precision aerial surveys with a root mean square error (RMSE) of less than 4 cm for three dimensions without using control points. After adding one GCP, the RMSE of three dimensions was close to that of traditional aerial survey methods using 12 GCPs, having a vertical accuracy of 2.51 cm. The same 99 VPs were used to evaluate the accuracy of the digital surface model produced using UAS-PPK. The results showed that the accuracy was close to that of traditional aerial survey methods, having an average error of less than 3 cm. We demonstrated the self-made attachable UAS-PPK module to be a reliable and accurate survey tool in geoscience applications.

TERRESTRIAL 3D MAPPING OF FORESTS: GEOREFERENCING CHALLENGES AND SENSORS COMPARISONS

Abstract. Terrestrial 3D reconstruction is a research topic that has recently received significant attention in the forestry sector. This practice enables the acquisition of high-quality 3D data, which can be used not only to derive physical forest criteria such as tree positions and diameters, but also more detailed analyses related to ecological parameters such as habitat availability and biomass. However, several challenges must be addressed before fully integrating this technology into forestry practices. The primary challenge is accurately georeferencing surveyed 3D data acquired in the same location and placing them into a national projection reference system. Unfortunately, due to the forest canopy, the GNSS signal is often obstructed, and it cannot guarantee sub-meter accuracy. In this paper, we have implemented an indirect georeferencing methodology based on spheres with known coordinates placed at the forest’s edge where GNSS reception was more reliable and accurate than under the canopy. We evaluated its performance through three analyses that confirmed the validity of our approach. Indeed, the accuracy of the TLS point cloud, georeferenced using our method, is within a centimetre level (4.7 cm), whereas mobile scanning methods demonstrate accuracy within the decimetre range but still less than a metre. Additionally, we have initiated the analysis of a potential future application for mixed reality headsets, which could enable real-time acquisition and visualisation of 3D data.

The impact of oblique images and flight‐planning scenarios on the accuracy of UAV 3D mapping

AbstractThe developments in lightweight unmanned aerial vehicles (UAVs) and structure‐from‐motion (SfM)‐based software have opened a new era in 3D mapping which is notably cost‐effective and fast, though the photogrammetric blocks lead to systematic height error due to inaccurate camera calibration parameters particularly when the ground control points (GCPs) are few and unevenly distributed. The use of onboard Global Navigation Satellite System (GNSS) receivers (such as RTK‐ or PPK‐based devices which use the DGNSS technique) to obtain the accurate coordinates of camera perspective centres has reduced the need for ground surveys, nevertheless, the aforementioned systematic error was reported in the UAV photogrammetric blocks. In this research, three flight‐planning scenarios with oblique imagery in addition to the traditional nadir block were evaluated and processed with four different processing cases. Therefore, 16 various blocks with different overlaps, direct and indirect georeferencing approaches as well as flight‐planning scenarios were tested to examine and offer the best imaging network. The results denote that the combination of oblique images located on a circle in the centre of the block with the nadir block provides the best self‐calibration functionality and improves the final accuracy by 50% (from 0.163 to 0.085 m) for direct georeferenced blocks and by 40% (from 0.042 to 0.026 m) for indirect georeferenced blocks.

PPK PROCESSING TO BOOST UAS ACCURACY IN CADASTRAL MAPPING

Abstract. Unmanned Aerial Systems (UAS) are increasingly used in different applications, including 3D urban modelling, cadastral mapping, urban planning, GIS information system and other fields because of their advantages. As a consequence, UAS equipment is constantly developed to provide more accurate results in a more reliable mode. This paper aims to evaluate the performances of a low-cost UAS system, namely DJI Phantom 4 Pro v2 equipped with a TeoKIT GNSS PPK (post-processing kinematic) module for cadastral mapping purposes. Two fights (oblique and nadir) over a residential area at 60 m height were performed and some 100 ground points were used to derive RMSE accuracies. Comparison between GNSS-aided with PPK processing and indirect georeferencing processes are performed. Given a mobile laser scanner (MLS) point cloud as ground truth, comparison with UAS point clouds and manually digitized features are also performed and reported.

Accuracy assessment of high-resolution terrain data produced from UAV images georeferenced with on-board PPK positioning

In recent years, unmanned aerial vehicles (UAVs) have increased rapidly and successfully established themselves as a tool for the rapid collection of high-resolution images as baseline data in land cover studies and topographic mapping. In photogrammetry using the SfM-MVS method of processing captured images, indirect georeferencing of the digital data through ground control points (GCPs) is usually applied. But selecting, marking, and coordinating GCPs in hardto-reach terrains is time-consuming and sometimes dangerous or impossible. The main objective of this study is to evaluate the accuracy of high-resolution topographic data (HRTD) products of photogrammetric processing of PPK-directly georeferenced images by SfM-MVS workflow. Direct and indirect methods of georeferencing digital products are compared. The planimetric and vertical root mean square error (RMSE) in the position of the validation points were calculated by the differences between measured coordinates in dense point clouds, orthophoto mosaics, and terrain surfaces (DSM), and precisely measured coordinates of the validation points by GNSSRTK receivers. The analysis is based on a statistical evaluation of experimental data obtained from a TAROT X6-based hexacopter equipped with two different image sensor configurations: 1) Sony RX0 action camera and 2) Sony A6000 mirrorless camera, and 3) DJI Phantom 4 Pro quadcopter with integrated additional L1-GNSS module for direct georeferencing by PPKmethod. HRTD generation was performed with three block control configurations for each UAV: 1) Indirect georeferencing via GCP only, 2) PPK direct georeferencing without GCP, and 3) PPK georeferencing using one GCP. Our research showed that when using L1-GNSS onboard receivers for PPK-georeferencing without any GCPs, the point cloud's planimetric accuracy (RMSExy) was from 0.125 to 0.231 cm, depending on the UAV/camera configuration. However, two flight missions produced significant vertical offsets, most likely due to ionospheric disturbances affecting the resolution of phase cycle ambiguities in the single-frequency receivers used. When adding one control point in the PPK georeferencing method, the planimetric and vertical accuracy of the data is comparable to the indirect GCP referencing method. Furthermore, our results show that camera properties (i.e., focal length, resolution, sensor quality) affect the quality and accuracy of digital products. The HRTDs were also evaluated according to the ASPRS (American Photogrammetry and Remote Sensing Society) Standards for Accuracy of Digital Geospatial Data. Analyzing the accuracy of the HRTDs obtained with the experimental UAV/camera configurations for the test area, the present study shows that the PPK-SfM-MVS workflow can provide quality data with a centimeters accuracy of the photogrammetric products.

The Influence of Flight Direction and Camera Orientation on the Quality Products of UAV-Based SfM-Photogrammetry

Unmanned aerial vehicles (UAVs) can provide valuable spatial information products for many projects across a wide range of applications. One of the major challenges in this discipline is the quality of positioning accuracy of the resulting mapping products in professional photogrammetric projects. This is especially true when using low-cost UAV systems equipped with GNSS receivers for navigation. In this study, the influence of UAV flight direction and camera orientation on positioning accuracy in an urban area on the west bank of the Euphrates river in Iraq was investigated. Positioning accuracy was tested in this study with different flight directions and camera orientation settings using a UAV autopilot app (Pix4Dcapture software (Ver. 4.11.0)). The different combinations of these two main parameters (camera orientation and flight direction) resulted in 11 different flight cases for which individual planimetric and vertical accuracies were evaluated. Eleven flight sets of dense point clouds, DEMs, and ortho-imagery were created in this way to compare the achieved positional accuracies. One set was created using the direct georeferencing method (without using GCPs), while the other ten sets were created using the indirect georeferencing approach based on ground truth measurements of five artificially created GCPs. Positional accuracy was found to vary depending on the user-defined flight plan settings, despite an approximately constant flight altitude. However, it was found that the horizontal accuracy achieved was better than the vertical accuracy for all flight sets. This study revealed that combining multiple sets of images with different flight directions and camera orientations can significantly improve the overall positional accuracy to reach several centimeters.

Assessment of Using UAV Imagery over Featureless Surfaces for Topographic Applications

The growth and development of Unmanned Aerial Vehicles (UAVs) as a photogrammetric platform, concurrently with the advances in Computer Vision (CV) and image processing algorithms have resulted using UAV Photogrammetry in several topographic applications. CV software algorithms rely on extracting, describing, and matching tie points from the sequences overlapping images to generate 3D colored point clouds. One of the biggest problems obstructing the automated processing of UAV imagery is the featureless of the covered surface. This paper has provided the ability, results, and accuracy of processing images captured by UAVs over non-textured sandy surfaces by providing four aligning and geo-referencing techniques. These four methods, IG/blind matching, IG/reference matching, DG/blind matching, and DG/reference matching, have been presented and tested for 630 aerial images with 80 % overlap and 80 % side lap covered approximately 1 km2 at altitude 178 m above ground level (AGL). The results showed that the captured images could be used to extract the photogrammetric topographical measurements with reliable accuracy. The four techniques' geometric accuracy has ranged between (0.043 m to 0.076 m) & (0.047 m to 0.074) for generated point clouds and linear exterior orientation (EO) parameters, respectively. The indirect geo-referencing with reference matching (IG/reference) recorded the highest-level accuracy of point clouds with 0.043m RMSE compared to the direct geo-referencing with reference matching (DG/reference) which gave the highest geometric accuracy of the linear EO parameters with 0.047m

New Supplementary Photography Methods after the Anomalous of Ground Control Points in UAV Structure-from-Motion Photogrammetry

Recently, multirotor UAVs have been widely used in high-precision terrain mapping, cadastral surveys and other fields due to their low cost, flexibility, and high efficiency. Indirect georeferencing of ground control points (GCPs) is often required to obtain highly accurate topographic products such as orthoimages and digital surface models. However, in practical projects, GCPs are susceptible to anomalies caused by external factors (GCPs covered by foreign objects such as crops and cars, vandalism, etc.), resulting in a reduced availability of UAV images. The errors associated with the loss of GCPs are apparent. The widely used solution of using natural feature points as ground control points often fails to meet the high accuracy requirements. For the problem of control point anomalies, this paper innovatively presents two new methods of completing data fusion by supplementing photos via UAV at a later stage. In this study, 72 sets of experiments were set up, including three control experiments for analysis. Two parameters were used for accuracy assessment: Root Mean Square Error (RMSE) and Multiscale Model to Model Cloud Comparison (M3C2). The study shows that the two new methods can meet the reference accuracy requirements in horizontal direction and elevation direction (RMSEX = 70.40 mm, RMSEY = 53.90 mm, RMSEZ = 87.70 mm). In contrast, the natural feature points as ground control points showed poor accuracy, with RMSEX = 94.80 mm, RMSEY = 68.80 mm, and RMSEZ = 104.40 mm for the checkpoints. This research considers and solves the problems of anomalous GCPs in the photogrammetry project from a unique perspective of supplementary photography, and proposes two new methods that greatly expand the means of solving the problem. In UAV high-precision projects, they can be used as an effective means to ensure accuracy when the GCP is anomalous, which has significant potential for application promotion. Compared with previous methods, they can be applied in more scenarios and have higher compatibility and operability. These two methods can be widely applied in cadastral surveys, geomorphological surveys, heritage conservation, and other fields.

A Comparative Analysis of Unmanned Aircraft Systems in Low Altitude Photogrammetric Surveys

Comparing photogrammetric performances of four user-grade unmanned aircraft systems (UAS) is the main aim of this paper. This study investigates what is the more suitable UAS for specific applications considering the required scale factor, such as for architectural, environmental and restoration purposes. Some photogrammetric surveys were conducted in a 5 ha area using a Phantom 4 Adv, Mavic 2 Pro, Mavic Air 2 and Mavic Mini 2. These unmanned aircrafts are commercial systems used mainly by private professionals. Some photogrammetric reconstructions were carried out by varying flight altitude and camera settings of the 4 UAS. Structure-from-motion (SfM) algorithms were applied to the images taken from the UASs. The surveys’ quality was analyzed by comparing the ground targets’ coordinates measured on the field with indirect georeferencing through global navigation satellite system (GNSS). Fifty targets were installed and arranged following a kind of regular grid. For each photogrammetric flight, the boundary conditions were maintained the same, as well as the flight trajectories and the ground control point distribution. Altimetric and planimetric residuals were reported and compared for each photogrammetric survey. Using a regular grid of ground targets, the result obtained from Phantom 4 is one order of magnitude better than the ones obtained from the other UASs. Mavic Mini 2 leads to an error average of about 5 cm. Remembering that the Mavic Mini 2 is an ultralight drone (it does not require a pilot’s license), it could significantly reduce costs compared to all the others.

Performing low-altitude photogrammetric surveys, a comparative analysis of user-grade unmanned aircraft systems

This paper shows results of comparing performances of four unmanned aircraft systems (UAS) in terms of photogrammetric survey’s quality. This study aims to investigate what is the more suitable UAS for specific applications considering the required scale factor, such as for architectural, environmental, and restoration purposes. A series of photogrammetric surveys were conducted in a hilly area of about 5 ha using Phantom 4 Adv, Mavic 2 Pro, Mavic Air 2, and Mavic Mini 2. These unmanned aircrafts are commercial user–grade systems used mainly by private professionals. Several photogrammetric reconstructions were performed by varying essential parameters, such as flight altitude and cameras of remotely piloted aircraft systems (RPAS), applying structure-from-motion (SfM) algorithms to the images taken from the UAS. The surveys’ quality was analyzed by comparing the ground targets’ coordinates extrapolated from the point clouds to those measured on the field with indirect georeferencing through GNSS technology. Fifty targets were installed and arranged following a reasonably regular mesh. The boundary conditions were maintained the same for each flight mission, flight trajectories, and the ground control point distribution on the ground. For each survey made by each of the four UAS, altimetric and planimetric residuals were reported and compared. Average residuals from Phantom 4 Adv, about 15 mm, almost disappear compared to the other UASs; the discrepancy is one order of magnitude. With a regular grid geometry of ground targets, the Mavic Mini 2 led to an error average of about 5 cm. Remembering that the Mavic Mini 2 is an ultralight drone (does not require a pilot's license), it could significantly reduce cost compared to the other systems.

Accuracy Assessment of UAV-Photogrammetric-Derived Products Using PPK and GCPs in Challenging Terrains: In Search of Optimized Rockfall Mapping

Unmanned aerial photogrammetric surveys are increasingly being used for mapping and studying natural hazards, such as rockfalls. Surveys using unmanned aerial vehicles (UAVs) can be performed in remote, hardly accessible, and dangerous areas, while the photogrammetric-derived products, with high spatial and temporal accuracy, can provide us with detailed information about phenomena under consideration. However, as photogrammetry commonly uses indirect georeferencing through bundle block adjustment (BBA) with ground control points (GCPs), data acquisition in the field is not only time-consuming and labor-intensive, but also extremely dangerous. Therefore, the main goal of this study was to investigate how accurate photogrammetric products can be produced by using BBA without GCPs and auxiliary data, namely using the coordinates X0, Y0 and Z0 of the camera perspective centers computed with PPK (Post-Processing Kinematic). To this end, orthomosaics and digital surface models (DSMs) were produced for three rockfall sites by using images acquired with a DJI Phantom 4 RTK and the two different BBA methods mentioned above (hereafter referred to as BBA_traditional and BBA_PPK). The accuracy of the products, in terms of the Root Mean Square Error (RMSE), was computed by using verification points (VPs). The accuracy of both BBA methods was also assessed. To test the differences between the georeferencing methods, two statistical test were used, namely a paired Student’s t-test, and a non-parametric Wilcoxon signed-rank. The results show that the accuracy of the BBA_PPK is inferior to that of BBA_traditional, with the total RMSE values for the three sites being 0.056, 0.066, and 0.305 m, respectively, compared to 0.019, 0.036 and 0.014 m obtained with BBA_traditional. The accuracies of the BBA methods are reflected in the accuracy of the orthomosaics, whose values for the BBA_PPK are 0.039, 0.043 and 0.157 m, respectively, against 0.029, 0.036 and 0.020 m obtained with the BBA_traditional. Concerning the DSM, those produced with the BBA_PPK method present accuracy values of 0.065, 0.072 and 0.261 m, respectively, against 0.038, 0.060 and 0.030 m obtained with the BBA_traditional. Even though that there are statistically significant differences between the georeferencing methods, one can state that the BBA_PPK presents a viable solution to map dangerous and exposed areas, such as rockfall transit and deposit areas, especially for applications at a regional level.

Improving Linear Projects Georeferencing to Create Digital Models Using UAV Imagery

Global Positioning System (GPS) on Unmanned Aerial Vehicles (UAV) platform relies on Micro Electro Mechanical Systems (MEMS) technology with a precision of 10 m at shooting time at UAV camera stations positions. Nonetheless, obstacles to the GPS signal at the finest flight altitude can prevent accurate camera stations positions retrieval. In this research, three different georeferencing techniques were compared with geometric precision. The first is Direct Georeferencing (DG), which mainly depends on using Navigation GPS onboard without using any Ground Control Points (GCPs). The second is Indirect Georeferencing (IG), which mainly depends on three GCPs used to assist Aero-Triangulation (AT). The third is Modified technique depends on the same three GCPs used in the second method and enhanced location of camera stations usage of the Linear Relation Model (LR Model). The study area was in the south of the Moscow Region, Russia. Threeimaging strips have been taken using the DJI PHANTOM 4 PRO UAV. The accuracy assessment was carried out using image-derived coordinates and checkpoints (CPs) residuals. This study emphasizes that the Modified methodology using enhanced camera stations positions gave better accuracy than using the drone GPS camera stations positions.

Assessing the georeferencing accuracy of different amount of image stripes for linear UAV projects

At present, the results of photogrammetric processing of images obtained from UAVs (orthophoto mosaics, digital elevation models, etc.) are widely used for environmental studies. Such materials are especially relevant and in demand for environmental monitoring of hard-to-reach objects. In addition, UAV survey materials are indispensable for impact monitoring, in which observation, assessment and forecast of the state of the natural environment in areas where hazardous and potentially hazardous (NPP) sources of anthropogenic impact are located are carried out. Regardless of the method of georeferencing of images - direct or indirect - the accuracy of the generated product is evaluated by ground control points. The purpose of this study is to assess the accuracy of photogrammetric constructions depending on the number of strips when surveying linear objects from UAVs and on the number of control points used in indirect georeferencing. Five groups of experiments were carried out during the study, three in each group with a different number of strips (from one to three). Five groups are conventionally combined into two sections. In the first section, direct and indirect georeferencing techniques were used with three locally located control points. In the second section, the method of indirect georeferencing was used with a different number of ground control points: six, twelve and thirty-four. Estimates of the accuracy of various tests have shown that an increase in the number of strips does not always lead to an increase in accuracy.

A Phase-Congruency-Based Scene Abstraction Approach for 2D-3D Registration of Aerial Optical and LiDAR Images

Registration of aerial images to enrich 3-D light detection and ranging (LiDAR) points with radiometric information can enhance the capability of object detection, scene classification, and semantic segmentation. However, airborne LiDAR data may not always come with on-board optical images collected during the same flight mission. Indirect georeferencing can be adopted, if ancillary imagery data are found available. Nevertheless, automatic recognition of control primitives in LiDAR and imagery datasets becomes challenging, especially when they are collected on different dates. This article proposes a generic registration mechanism based on using the phase congruency (PC) model and scene abstraction to overcome the stated challenges. The approach relies on the use of a PC measure to compute the image moments that determine the study scene's edges. Potential candidate points can be identified based on thresholding the image moments' values. A shape context descriptor is adopted to automatically pair symmetric candidate points to produce a final set of control points. Coordinate transformation parameters between the two datasets were estimated using a least squares adjustment for four registration models: first- (affine), second-, third-order polynomials, and direct linear transform models. Datasets covering different urban landscapes were used to examine the proposed workflow. The root-mean-square error of the registration is between one and two pixels. The proposed workflow is found to be computationally efficient especially with small-sized datasets, and generic enough to be applied in registering various imagery data and LiDAR point clouds.