Comprehensive Direct Georeferencing of Aerial Images for Unmanned Aerial Systems Applications

Optical image sensors are the most common remote sensing data acquisition devices present in Unmanned Aerial Systems (UAS). In this context, assigning a location in a geographic frame of reference to the acquired image is a necessary task in the majority of the applications. This process is denominated direct georeferencing when ground control points are not used. Despite it applies simple mathematical fundamentals, the complete direct georeferencing process involves much information, such as camera sensor characteristics, mounting measurements, attitude and position of the UAS, among others. In addition, there are many rotations and translations between the different reference frames, among many other details, which makes the whole process a considerable complex operation. Another problem is that manufacturers and software tools may use different reference frames posing additional difficulty when implementing the direct georeferencing. As this information is spread among many sources, researchers may face difficulties on having a complete vision of the method. In fact, there is absolutely no paper in the literature that explain this process in a comprehensive way. In order to supply this implicit demand, this paper presents a comprehensive method for direct georeferencing of aerial images acquired by cameras mounted on UAS, where all required information, mathematical operations and implementation steps are explained in detail. Finally, in order to show the practical use of the method and to prove its accuracy, both simulated and real flights were performed, where objects of the acquired images were georeferenced.

Accuracy Assessment for points coordinates surveyed using low-cost Unmanned Aerial Vehicle and Global Positioning System with 3Dsurvey and 3DF Zephyr software

Ground Control Points GCPs are the only way to obtain accurate positions in aerial surveys. At least three points should be utilized, and the model will get increasingly accurate in X, Y, and Z coordinates as the number rises. The accuracy of the 3D model created from aerial photography is also affected by the arrangement of GCPs. The goal of this research is to determine the optimal number and arrangement of GCPs in order to obtain the lowest possible error in point positioning. A conventional UAV called DJI Mavic 2 pro was used to photograph one and a half square kilometer site at an elevation equal to hundred meters from earth’s surface with nadir camera configuration. GSD (ground sampling distance) of 2.3 centimeters was used to collect 1515 pictures. 62 GCPs were observed in PPK (Post Processing kinematic) method using a DGPS (differential global positioning system) receiver GS 15 from Leica. The study area was split into two areas, one with a straight arrangement of GCPs and the other with a diagonal arrangement of GCPs. The pictures were processed using 3Dsurvey and 3DF Zephyr software utilizing a full bundle adjustment procedure with increasing GCPs number beginning with three GCPs and ending with twenty-six GCPs for both arrangement layout, with the other points serving as check points for the model’s accuracy at each attempt. The check point coordinates obtained were compared to the DGPS coordinates. The result indicates the optimal GCP number needed for the most accurate position and spread layout. That the minimum gap between adjacent GCPs ought to be not over than 100 meters and spread homogenously.

High-Precision Digital Surface Model Extraction from Satellite Stereo Images Fused with ICESat-2 Data

The lack of ground control points (GCPs) affects the elevation accuracy of digital surface models (DSMs) generated by optical satellite stereo images and limits the application of high-resolution DSMs. It is a feasible idea to use ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) laser altimetry data to improve the elevation accuracy of optical stereo images, but it is necessary to accurately match the two types of data. This paper proposes a DSM registration strategy based on terrain similarity (BOTS), which integrates ICESat-2 laser altimetry data without GCPs and improves the DSM elevation accuracy generation from optical satellite stereo pairs. Under different terrain conditions, Worldview-2, SV-1, GF-7, and ZY-3 stereo pairs were used to verify the effectiveness of this method. The experimental results show that the BOTS method proposed in this paper is more robust when there are a large number of abnormal points in the ICESat-2 data or there is a large elevation gap between DSMs. After fusion of ICESat-2 data, the DSM elevation accuracy extracted from the satellite stereo pair is improved by 73~92%, and the root mean square error (RMSE) of Worldview-2 DSM reaches 0.71 m.

Estimation of the Setting and Infrastructure Criterion of the UI GreenMetric Ranking Using Unmanned Aerial Vehicles

This study presents a methodology to estimate the seven indicators of the Setting and Infrastructure criterion of the UI GreenMetric World University Ranking based on three-dimensional data from a point cloud taken from an unmanned aerial vehicle (UAV). This study also estimated the potential aerial biomass, C and CO2, stored in the green spaces of a university campus using photogrammetric data analyzed in a Geographic Information System (GIS). The method was based on isolating classified point clouds using digital surface models (DSMs) and ground control points (GCPs) considering the canopy height model (CHM), the allometric equation (DBH, p, h), the biomass conversion factor, and carbon dioxide equivalents (CO2-e). The results confirmed that the national models for estimating the potential C reserves in natural forests are very close to reality and that the open space and green areas available to people on campus are adequate. The use of photogrammetric data facilitated the estimation of UI GreenMetric indicators from a highly detailed, low-cost three-dimensional model. The results of a case study revealed that the campus assimilates the CO2 emissions it produces and generates a surplus.

Improving the Accuracy of Aerial Photography Using Ground Control Points

The authors showed that it is possible to quickly collect up-to-date information on the agricultural land condition using an unmanned aerial vehicle. It was noted that the use of ground control points increases the accuracy of project measurements, helps to compare the project post-processing results with the real measurements. (Research purpose) To compare the results of standard and high-precision post-processing of aerial survey data using ground control points. (Materials and methods) Aerial photography was carried out on a 1.1- hectare breeding field. The authors used DJI Matrice 200 v2 unmanned aerial vehicle with a GNSS L1/L2 receiver and a modified DJI X4S camera, five control points sized 50 × 50 centimeters and an EMLID Reach RS2 multi-frequency GNSS receiver. The results of scientific research into the use of ground control points during aerial photography were studied. (Results and discussion) It was found out that the error of georeferencing images obtained by an unmanned aerial vehicle without control points is significantly higher during the standard data processing compared to the high-precision one. The project error when using five control points is 3.9 times higher during the standard data processing. (Conclusions) It was shown that using ground control points it is possible to improve the project measurement accuracy, as well as compare the project post-processing results with the measurements on the ground. It was detected that the high-precision monitoring enables the use of fewer ground control points. It was found out that in order to obtain data with the accuracy of 2-4 centimeters in plan and height, at least 3 ground control points need to be used during the high-precision post-processing.

Photogrammetry (SfM) vs. Terrestrial Laser Scanning (TLS) for Archaeological Excavations: Mosaic of Cantillana (Spain) as a Case Study

The discovery of a Roman mosaic from the 2nd century AD in Cantillana (Seville) generated interest and the need for exhaustive documentation, so that it could be recreated with real measurements in a 3D model, not only to obtain an exact replica, but with the intention of analyzing and studying the behavior of two main geomatics techniques. Thus, the objective of this study was the comparative analysis of both techniques: near object photogrammetry by SfM and terrestrial laser scanner or TLS. The aim of this comparison was to assess the use of both techniques in archaeological excavations. Special attention was paid to the accuracy and precision of measurements and models, especially in altimetry. Mosaics are frequently relocated from their original location to be exhibited in museums or for restoration work, after which they are returned to their original place. Therefore, the altimetric situation is of special relevance. To analyze the accuracy and errors of each technique, a total station was used to establish the real values of the ground control points (GCP) on which the comparisons of both methods were to be made. It can be concluded that the SfM technique was the most accurate and least limiting for use in semi-buried archaeological excavations. This manuscript opens new perspectives for the use of SfM-based photogrammetry in archaeological excavations.

Combination of Terrestrial and Satellite Topography for Pipeline Engineering and Construction

A method consisting in an optimal combination of conventional topography from a terrestrial acquisition and satellite derived topography is presented. The solution recently implemented in the UAE for the engineering and the construction of a gas export pipeline allows significant cost reduction, time saving, and safety hazard reduction as fewer terrestrial operations are needed. The survey area is split into 2 sub-areas: area with infrastructures requiring a high accuracy is surveyed with terrestrial topographical acquisition methods such as GNSS receivers, the other one with desert conditions is mapped from satellite stereoscopic imagery. Stereoscopic mode refers to when the satellite sensor acquires two images of the same location taken from different angles. Using photogrammetric techniques, it produces a 3D elevation model of the area. The native satellite imagery allows a mapping of the surface features as well. Terrestrial and satellite datasets are finally merged and adjusted to provide engineering and construction contractors with a unique survey dataset. Terrestrial survey methods provide generally 5-10cm horizontal and vertical accuracies whereas satellite topography has accuracy of a few meters, so satellite topography must be controlled and adjusted from terrestrial ground control points which allow to reach an average 50cm absolute accuracy. This is good enough in desert areas with neither particular ground feature nor steep relief requiring complex design. Satellite acquisition has limitations: vegetation masking the ground, steep slopes and dense infrastructures. It is therefore necessary to combine conventional and satellite topography to meet engineering requirements. This is considered when defining the satellite and terrestrial survey areas. Beyond these limitations, this solution has strong advantages. Satellite grid resolution can be better (1-2m versus 5-10m for GNSS surveys). Acquisition and processing are faster (about 2 weeks versus a few weeks or months), and costs are from 10 to 100 times cheaper than conventional methods. No need for personnel and equipment on site, no management of logistics and permitting as well. Finally, it reduces safety hazards such as car accident, harsh weather, manual handling, etc. In addition, limiting the area to be surveyed with conventional equipment may avoid the need to mobilize Airborne photogrammetry or lidar systems usually operated by foreign companies. This limits complex Call for Tender, permitting management and give more opportunity to contract local companies. Satellite topography is widely used for preliminary studies, but the innovation here consists in an optimal combination of terrestrial and satellite datasets for engineering and construction purposes. This solution has however some limitations as it requires suitable conditions for satellite optical imagery acquisitions: no vegetation, limited cloud cover, smooth topography, and limited infrastructures. This is of interest basically in Middle east and North Africa.

Accuracy Comparison and Assessment of DSM Derived from GFDM Satellite and GF-7 Satellite Imagery

Digital Surface Model (DSM) derived from high resolution satellite imagery is important for various applications. GFDM is China’s first civil optical remote sensing satellite with multiple agile imaging modes and sub-meter resolution. Its panchromatic resolution is 0.5 m and 1.68 m for multi-spectral images. Compared with the onboard stereo viewing instruments (0.8 m for forward image, 0.65 m for back image, and 2.6 m for back multi-spectrum images) of GF-7, a mapping satellite of China in the same period, their accuracy is very similar. However, the accuracy of GFDM DSM has not yet been verified or fully characterized, and the detailed difference between the two has not yet been assessed either. This paper evaluates the DSM accuracy generated by GFDM and GF-7 satellite imagery using high-precision reference DSM and the observations of Ground Control Points (GCPs) as the reference data. A method to evaluate the DSM accuracy based on regional DSM errors and GCPs errors is proposed. Through the analysis of DSM subtraction, profile lines, strips detection and residuals coupling differences, the differences of DSM overall accuracy, vertical accuracy, horizontal accuracy and the strips errors between GFDM DSM and GF-7 DSM are evaluated. The results show that the overall accuracy of both is close while the vertical accuracy is slightly different. When regional DSM is used as the benchmark, the GFDM DSM has a slight advantage in elevation accuracy, but there are some regular fluctuation strips with small amplitude. When GCPs are used as the reference, the elevation Root Mean Square Error (RMSE) of GFDM DSM is about 0.94 m, and that of GF-7 is 0.67 m. GF-7 DSM is more accurate, but both of the errors are within 1 m. The DSM image residuals of the GF-7 are within 0.5 pixel, while the residuals of GFDM are relatively large, reaching 0.8 pixel.

Performance of UAV-Based Digital Orthophoto Generation for Emergency Response Applications

Unmanned Aerial Vehicles (UAVs) have been used for accurate orthophoto generation based on advanced Global Navigation Satellite System (GNSS) techniques. In recent years, the UAV systems have become an effective tool for fast monitoring of damages caused by disasters such as the earthquake hazards. The conventional orthophoto generation based on ground control points takes too much time during emergency situations. In the study, different methodologies for the processing of the acquired GNSS Positioning data for direct georeferencing of UAVs were investigated in terms of various orbit products. Evaluating the fitness for emergency response applications, the ground control points (GCPs) also used for validation of the generated orthophoto without using GCPs and based on Precise Point Positioning (PPP) approach. In this study, Ultra-Rapid, Rapid and Final PPP methods based on GNSS observations were used for direct geo-referencing. Thirteen GCPs were located at the study area for the validation of the orthophoto accuracy generated by direct geo-referencing.