Beach Leveling Using a Remotely Piloted Aircraft System (RPAS): Problems and Solutions

Georeferenced archival aerial images are key elements for the study of landscape evolution in the scope of territorial planning and management. The georeferencing process proceeds by applying to photographs advanced digital photogrammetric techniques integrated along with a set of ground truths termed ground control points (GCPs). At the end of that stage, the accuracy of the final orthomosaic is assessed by means of root mean square error (RMSE) computation. If the value of that index is deemed to be unsatisfactory, the process is re-run after increasing the GCP number. Unfortunately, the search for GCPs is a costly operation, even when it is visually carried out from recent digital images. Therefore, an open issue is that of achieving the desired accuracy of the orthomosaic with a minimal number of GCPs. The present paper proposes a geostatistically-based methodology that involves performing the spatialization of the GCP errors obtained from a first gross version of the georeferenced orthomosaic in order to produce an error map. Then, the placement of a small number of new GCPs within the sub-areas characterized by the highest local errors enables a finer georeferencing to be achieved. The proposed methodology was applied to 67 historical photographs taken on a geo-morphologically complex study area, located in Southern Italy, which covers a total surface of approximately 55,000 ha. The case study showed that 75 GCPs were sufficient to garner an orthomosaic with coordinate errors below the chosen threshold of 10 m. The study results were compared with similar works on georeferenced images and demonstrated better performance for achieving a final orthomosaic with the same RMSE at a lower information rate expressed in terms of nGCPs/km2.

Aim. Determination of the elements of external spatial orientation of the surveying systems at the moment of image acquisition is the fundamental task in photogrammetry. Principally, this problem is solving in two ways. The first way is direct positioning and measuring of directions of camera optical axis in the geodetic space with the help of GNSS/INS equipment. The second way is the analytical solution of the problem using a set of reference information (often such information is a set of ground control points whose geodetic positions are known with sufficient accuracy and which are reliably recognised on aerial images of the photogrammetric block). The authors consider the task of providing reference and control information using the second approach, which has a number of advantages in terms of reliability and accuracy of determining the unknown image exterior orientation parameters. It is proposed to obtain additional images of ground control points by the method of their auxiliary aerial photography using an unmanned aerial vehicle (UAV) on a larger scale compared to the scale of the images of the photogrammetric block. The aim of the presented work is the implementation of the method of creating reference points and experimental confirmation of its effectiveness for photogrammetric processing. Methods and results. For the entire realization of the potential of the analytical way to determine the elements of external orientation of images, it is necessary to have a certain number of ground control points (GCP) and to keep the defined scheme of their location on the photogrammetric block. As the main source of input data authors use UAV aerial images of the terrain, which are obtained separately from the block of aerial survey, and have a better geometric resolution and which clearly depict the control reference points. Application of such auxiliary images gives the possibility of automated transferring of the position of ground control point into images of the main photogrammetric block. In our interpretation, these images of ground control points and their surroundings on the ground are called "control reference images". The basis of the work is to develop a method for obtaining the auxiliary control reference images and transferring of position of GCP depicted on them into aerial or space images of terrain by means of computer stereo matching. To achieve this goal, we have developed a processing method for the creation of control reference images of aerial image or a series of auxiliary multi-scale aerial images obtained by a drone from different heights above the reference point. The operator identifies and measures the GCP once on the auxiliary aerial image of the highest resolution. Then there is an automatic stereo matching of the control reference image in the whole series of auxiliary images in succession with a decrease in the resolution and, ultimately, directly with the aerial images of photogrammetric block. On this stage there are no recognition/cursor targeting by the human operator, and therefore there are no discrepancies, errors or mistakes related to it. In addition, if to apply fairly large size of control reference images, the proposed method can be used on a low-texture terrain, and therefore deal in many cases without the physical marking of points measured by GNSS method. And this is a way to simplify and reduce the cost of photogrammetric technology. The action of the developed method has been verified experimentally to provide the control reference information of the block of archival aerial images of the low-texture terrain. The results of the experimental approbation of the proposed method give grounds to assert that the method makes it possible to perform geodetic reference of photogrammetric projects more efficiently due to the refusal of the physical marking of the area before aerial survey. The proposed method can also be used to obtain the information for checking the quality of photogrammetric survey for provision of check points. The authors argue that the use of additional equipment - UAV of semi-professional class to obtain control reference images is economically feasible. Scientific novelty and practical relevance. The results of approbation of the "control reference image" method with obtaining stereo pairs of aerial images with vertical placement of the base are presented for the first time. There was implemented the study of the properties of such stereo pairs of aerial images to obtain images of reference points. The effectiveness of including reference images in the main block of the digital aerial triangulation network created on UAV’s images is shown.

Recently, photogrammetric products produced using Unmanned Aerial Vehicles (UAVs) are frequently preferred for disaster management, geological, and meteorological studies, urban studies and military purposes. Obtaining products with UAV photogrammetry provides a significant advantage over ground-based methods in terms of speed and cost. At the same time, the accuracy of the products produced is of particular importance. In this study, the effects of the number of Ground Control Points (GCPs) and GCP network design on accuracy were investigated. In this direction, 12 GCPs with homogeneous distribution in the study area were established and their locations were determined by GNSS measurements. Then, scenarios with different number of GCPs and different network designs were created, and the accuracy of these points were evaluated. As a result, it has been concluded that 5 GCPs homogeneously distributed in the field is sufficient for the products at high accuracy (≤ 10 cm) required in geomatics applications, increasing the number of the GCPs will not benefit the accuracy of the project and this process is disadvantageous in terms of increasing the cost along with the speed.

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%.

Unmanned aerial vehicle (UAV) systems are often used to collect high-resolution imagery. Data obtained from UAVs are now widely used for both military and civilian purposes. This article discusses the issues related to the use of UAVs for the imaging of restricted areas. Two methods of developing single-strip blocks with the optimal number of ground control points are presented. The proposed methodology is based on a modified linear regression model and an empirically modified Levenberg–Marquardt–Powell algorithm. The effectiveness of the proposed methods of adjusting a single-strip block were verified based on several test sets. For method I, the mean square errors (RMSE) values for the X, Y, Z coordinates of the control points were within the range of 0.03–0.13 m/0.08–0.09 m, and for the second method, 0.03–0.04 m/0.06–0.07 m. For independent control points, the RMSE values were 0.07–0.12 m/0.06–0.07 m for the first method and 0.07–0.12 m/0.07–0.09 m for the second method. The results of the single-strip block adjustment showed that the use of the modified Levenberg–Marquardt–Powell method improved the adjustment accuracy by 13% and 16%, respectively.

Point cloud (PC) generation from photogrammetry–remotely piloted aircraft systems (RPAS) at high spatial and temporal resolution and accuracy is of increasing importance for many applications. For several years, photogrammetry–RPAS has been used to recover civil engineering works such as digital elevation models (DEMs), triangle irregular networks (TINs), contour levels, orthophotographs, etc. This study analyzes the influence of variables involved in the accuracy of PC generation over asphalt shapes and determines the most influential variable based on the development of an artificial neural network (ANN) with patterns identified in the test flights. The input variables were those involved, and output was the three-dimension root mean square error (3D-RMSE) of the PC in each ground control point (GCP). The result of the study shows that the most influential variable over PC accuracy is the modulation transfer function 50 (MTF50). In addition, the study obtained an average 3D-RMSE of 1 cm. The results can be used by the scientific and civil engineering communities to consider MTF50 variables in obtaining images from RPAS cameras and to predict the accuracy of a PC over asphalt based on the ANN developed. Also, this ANN could be the beginning of a large database containing patterns from several cameras and lenses in the world market.

Ground control points (GCPs) are used in the process of indirectly georeferencing Unmanned Aerial Systems (UAS) images. A minimum of three ground control points (GCPs) is required but increasing the number of GCPs will lead to higher accuracy of the final results. The aim of this study is to provide the answer to the question of how many ground control points are necessary in order to derive high precision results. To obtain the results, an area of about 1 ha was photographed with a low-cost UAS, namely, the DJI Phantom 3 Standard at two different heights, 28 m and 35 m above ground, the camera being oriented in a nadiral position, and 50 ground control points were measured using a total station. In the first and the second scenario, the UAS images were processed using the Pix4D Mapper Pro software and 3DF Zephyr, respectively, by performing a full bundle adjustment process with the number being gradually increased from three GCPs to 40. The third test was made with 3DF Zephyr Pro software using a free-network approach in the bundle adjustment. Also, the point clouds and the mesh surfaces derived automatically after using the minimum and the optimum number of GCPs, respectively, were compared with a terrestrial laser scanner (TLS) point cloud. The results expressed a clear overview of the number of GCPs needed for the indirect georeferencing process with minimum influence on the final results.

This work addresses the initiative to use consumer-grade Remotely Piloted Aircraft Systems (RPAS), known as Unmanned Aerial Vehicles (UAVs) or drones, as potential instruments for the evaluation and monitoring of shallow coral reefs in Colombia. This initiative started with the construction of an interdisciplinary team that includes institutions from academia, the Ministry of Defense, the Ministry of Environment and Sustainable Development, a marine surveying private company, and a German research center. The test was conducted inside the Marine Protected Area (MPA) of Old Providence; an island that is located at the Archipelago of San Andrs, Providencia and Santa Catalina, which includes the Seaflower Biosphere Reserve (the third largest true barrier reef in the world). Field data were acquired by using two DJI RPAS: Phantom 4 Pro and Mavic Air, and geo-referenced diving. In this work, we show the potential for using such technologies with a geometrically corrected mosaic of images of Crab Cay, a small cay located inside the MPA. This project is the first of its kind within the Seaflower Reserve and may support technology-based data collection, increasing the quality of information that can be useful to make important decisions regarding ocean space utilization. Additionally, obtained images will be shared for the benefit of local communities, other stakeholders, and visitors, in a manner that opens up science to broader society, promoting outreach activities and educational tool via photos and videos.

During the past years, unmanned aerial vehicles (UAVs) gained importance as a tool to quickly collect high-resolution imagery as base data for cadastral mapping. However, the fact that UAV-derived geospatial information supports decision-making processes involving people’s land rights ultimately raises questions about data quality and accuracy. In this vein, this paper investigates different flight configurations to give guidance for efficient and reliable UAV data acquisition. Imagery from six study areas across Europe and Africa provide the basis for an integrated quality assessment including three main aspects: (1) the impact of land cover on the number of tie-points as an indication on how well bundle block adjustment can be performed, (2) the impact of the number of ground control points (GCPs) on the final geometric accuracy, and (3) the impact of different flight plans on the extractability of cadastral features. The results suggest that scene context, flight configuration, and GCP setup significantly impact the final data quality and subsequent automatic delineation of visual cadastral boundaries. Moreover, even though the root mean square error of checkpoint residuals as a commonly accepted error measure is within a range of few centimeters in all datasets, this study reveals large discrepancies of the accuracy and the completeness of automatically detected cadastral features for orthophotos generated from different flight plans. With its unique combination of methods and integration of various study sites, the results and recommendations presented in this paper can help land professionals and bottom-up initiatives alike to optimize existing and future UAV data collection workflows.

Мета: Відсутність алгоритму рекомендацій дій оператора БПЛА в надзвичайних ситуаціях, розкладання процесу прийняття рішень оператором БПЛА в надзвичайних ситуаціях, розробка структури системи підтримки прийняття рішень для оператора БПЛА, розробка моделей прийняття рішень оператором БПЛА. Методи: Створення алгоритму прийняття рішень за допомогою критеріїв Вальда, Гурвіца, Ла Пласа. Результати: програма "UAV_AS", що дає рекомендації оператору БПЛА про те, як діяти в разі виникнення надзвичайної ситуації. Авторами розроблено алгоритм дій рекомендації оператора БПЛА в надзвичайних ситуаціях. Представлений процес прийняття рішень оператором БПЛА в надзвичайних ситуаціях у вигляді детермінованої моделі із застосуванням методів мережевого моделювання. Побудовано мережевий граф, за яким визначено критичний час на парирування надзвичайної ситуації у разі польоту БПЛА. Розроблено структуру розподіленої СППР для колективного управління пілотованих та безпілотних літальних апаратів оператором. Авторами розроблено бази даних для локальних СППР операторів управління БПЛА, Розроблено моделі прийняття рішень оператором БПЛА в умовах ризику та в умовах невизначеності у разі аварійної посадки БПЛА. Обговорення: У статті розглядаються проблеми польотів безпілотних літальних апаратів для вирішення різних завдань в надзвичайній ситуації, засноване на статистичних даних від аналізу типів надзвичайних ситуацій для безпілотних літальних апаратів. Визначається послідовність дій оператора БПЛА в разі виникнення надзвичайних ситуацій.

Identifying the optimal structure of terrain dependent Rational Function Models (RFMs) not only decreases the number of Ground Control Points (GCPs) required, but also increases the accuracy of the model, by reducing the multi-collinearity of Rational Polynomials Coefficients (RPCs) and avoiding the ill-posed problem. Global optimization algorithms such as Genetic Algorithm (GA), evaluate the different combinations of parameters effectively. Therefore, they have a high ability to detect the optimal structure of RFMs. However, one drawback of these algorithms is their high computation cost. This article proposes a knowledge-based search strategy to overcome this deficiency. The backbone of the proposed method relies on the technical knowledge about the geometric condition of image at the time of acquisition, as well as the effect of external factors such as terrain relief on the image. This method was evaluated on four different datasets, including a SPOT-1A, a SPOT-1B, an IKONOS-Geo image, and a GeoEye-Geo imagery, using various number of GCPs. Experimental results demonstrate the efficiency of the proposed method to achieve a sub-pixel accuracy using few GCPs (only 4–6 points) in all datasets. The results also indicate that the proposed method improves the computation speed by 140 times when comparing with GA.

Structure-from-Motion (SfM) photogrammetry is increasingly employed in geomorphological applications for change detection, but repeatability and reproducibility of this methodology are still insufficiently documented. This work aims to evaluate the influence of different survey acquisition and processing conditions, including the camera used for image collection, the number of Ground Control Points (GCPs) employed during Bundle Adjustment, GCP coordinate precision and Unmanned Aerial Vehicle flight mode. The investigation was carried out over three fluvial study areas characterized by distinct morphology, performing multiple flights consecutively and assessing possible differences among the resulting 3D models. We evaluated both residuals on check points and discrepancies between dense point clouds. Analyzing these metrics, we noticed high repeatability (Root Mean Square of signed cloud-to-cloud distances less than 2.1 cm) for surveys carried out under the same conditions. By varying the camera used, instead, contrasting results were obtained that appear to depend on the study site characteristics. In particular, lower reproducibility was highlighted for the surveys involving an area characterized by flat topography and homogeneous texturing. Moreover, this study confirms the importance of the number of GCPs entering in the processing workflow, with different impact depending on the camera used for the survey.

This research demonstrates the potential and evaluates the conditions of experiment of SPOT image path and bloc adjustment using a 3D physical model for geometric correction of multi-source images developed at the Canada Centre for Remote Sensing. In a first step, image paths are automatically generated with the images acquired from the same orbit. Results with 11 panchromatic and 6 multiband SPOT images show that the same accuracy can be obtained with large image blocks as with a single image using a limited number of ground control points (GCPs) and tie points in all image overlaps. When the viewing angle differences of adjacent images or paths are less than 30°, the elevation should be added to these tie points, because the intersection geometry is too weak. When the viewing angle differences of adjacent images or paths are less than 6°, the final accuracy of the block can be reduced by a factor of six, and GCPs have to be added on these images to recover the initial block accuracy.

With increasing demands in real-time or near real-time remotely sensed imagery applications in such as military deployments, quick response to terrorist attacks and disaster rescue, the on-board geometric calibration problem has attracted the attention of many scientists in recent years. This paper presents an on-board geometric calibration method for linear CCD sensor arrays using FPGA chips. The proposed method mainly consists of four modules—Input Data, Coefficient Calculation, Adjustment Computation and Comparison—in which the parallel computations for building the observation equations and least squares adjustment, are implemented using FPGA chips, for which a decomposed matrix inversion method is presented. A Xilinx Virtex-7 FPGA VC707 chip is selected and the MOMS-2P data used for inflight geometric calibration from DLR (Köln, Germany), are employed for validation and analysis. The experimental results demonstrated that: (1) When the widths of floating-point data from 44-bit to 64-bit are adopted, the FPGA resources, including the utilizations of FF, LUT, memory LUT, I/O and DSP48, are consumed at a fast increasing rate; thus, a 50-bit data width is recommended for FPGA-based geometric calibration. (2) Increasing number of ground control points (GCPs) does not significantly consume the FPGA resources, six GCPs is therefore recommended for geometric calibration. (3) The FPGA-based geometric calibration can reach approximately 24 times faster speed than the PC-based one does. (4) The accuracy from the proposed FPGA-based method is almost similar to the one from the inflight calibration if the calibration model and GCPs number are the same.

The use of lightweight Unmanned Aerial Vehicle with the aerial photogrammetry approach to construct the Digital Surface Model (DSM) has been effectively applied for various types of topography. However, the ability to carry out this approach for huge active open coal mines is insufficiently investigated, furthermore, the influences of topographical factors on the accuracy of DSM are ambiguous. This experiment attempts to apply the UAV method for the two active coal mines with the total area of 7.99 km2 , exploited at a range from -300 m to 300 m altitude to figure out the effect of topographic factors on the accuracy of DEM constructed from UAV images. A total of 972 UAV images and 17 ground control points have been coupled to construct DSM of the mines. Besides, 16 checking points located at different elevations are used to evaluate the accuracy of DEM and to define the influence. DEMs are generated with the maximum RMSE of 0.086 m, 0.099 m, and 0.170 m corresponding to X, Y, and Z dimensional errors. The results show the unclear correlation between the vertical accuracy of DEM and the relative elevation (R2=0.064), the general slope of the mines, and the number of ground control points using in the coal mines as well.