Analysis and comparison of the exactness of specialist drone-based software products in urban and exurban region

Unmanned Aerial System (UAS) photogrammetry now provides a low-cost, fast, and effective approach to real-time acquisition of high resolution and digital geospatial information, as well as automatic 3D modeling of objects, for a variety of applications including topographical mapping, 3D city modelling, orthophoto generation, and cultural heritage preservation. UASs are known by a variety of names and acronyms, including aerial robots or simply drones, with UAV and drone being the most commonly used terminology. Because of the versatility of their on-board Global Navigation Satellite System (GNSS) navigation systems and inertial measurement unit (IMU) sensors, UASs open up new options for photogrammetric projects. In this research, the ability of four different state-of-the-art and professional drone-based software packages, including AgisoftMetashape, InphoUASmaster, Photomodeler UAS, and Pix4D Mapper, to generate a high density point cloud as well as a Digital Surface Model (DSM) and true orthoimage over barren, residential, green space, and uniform textured areas in urban and exurban areas is investigated.

The following are the major processes in this study: image acquisition, point cloud, DSM, DEM generation, and accuracy assessment. Data planning and acquisition are the initial steps in commencing any project. The overlapping images are initially obtained using four data sets with distinct surface feature attributes and camera kinds with different shooting situations. The data sets that must be acquired include pictures taken with FC6310 (8.8 mm), NEX-5R (5.2 mm), and Canon IXUS 220HS (4.3 mm) cameras at varied flight heights and spatial resolutions ranging from 52 to 246 m. The four data sets, two of which are connected to Iran and two of which are related to other nations, were chosen from barren, residential, green space, and uniform texture areas. GPS coordinates for these photos must also be recorded using a GPS device. This is done to geo-reference the images for improved model accuracy. The calibration of the camera must also be addressed, and its characteristics and readings must be determined at the start of the project. The images will be calibrated first in order to determine camera pose estimate. The following stage is to compare survey measurements to model measurements in order to assess the overall correctness of the 3D model. The correctness of the point cloud, DSM, and 3D textured model is next evaluated. The accuracy evaluation evaluates the orientation correctness, and measurement uncertainties in the various modeling procedures. Finally, the various products of the mentioned software packages were statistically and qualitatively evaluated.

The outcomes of this study demonstrate the ability of commercial photogrammetric software packages to do automatic 3D reconstruction of numerous attributes across urban and exurban regions using high quality aerial imagery. This assessment employs a variety of visual and geometric measurements to assess the quality of produced point clouds as well as the performance of the four software packages. According to the visual quality findings, AgisMesh software performs better in 3D modeling of all varieties of surfaces in all locations, but badly in the reconstruction of building edges in urban regions. Pix4D software, on the other hand, performs poorly in areas with uniform texture but excels at recognizing height changes and reconstructing building site boundaries. In terms of visual outcomes, the other software falls somewhere in the middle. In quantitative tests, they were tested first with checkpoints and then with randomly selected points in three distinct classes of urban and exurban regions. Check point findings revealed that the root mean square error (RMSE) in AgisMesh, UASmas, Pix4D, and PhUAS software packages was 2.82, 2.63, 5.84, and 3.03 cm, respectively. Furthermore, quantitative findings obtained by choosing random locations revealed that UASmas had an accuracy of 1.83, 1.20, and 2.74 cm, respectively, in three residential, barren, and green space zones. In addition to the 6.90, 2.96, and 7.24 cm accuracy of the PhUAS, the Pix4D was 4.72, 3.46, and 3.59 cm more accurate than AgisMesh software in the three stated classes. Table 1 displays the assessment findings based on the RMSE criterion.

The findings of this study indicate the capacity of specialist drone-based photogrammetric software packages to automatically reconstruct 3D features from high quality aerial images over desolate, residential, green space, and uniform texture environments. In this study, all conditions and parameters in all software were regarded the same, and owing to the similarity of statistical parameters, number of points, and so on in various products, only the discrepancies and their differences were discussed in depth. Various visual and geometric parameters are utilized in this evaluation to analyze the quality of generated 3D point clouds, DSM, and true orthophoto. AgisMesh offers a simple and easy user interface in general and visual assessment, and it is possible to describe and execute data from any camera, even unknown models, without utilizing coordinate images by utilizing powerful processing methods. In contrast, the UASmas program has a highly complex user interface, and the user must be familiar with all of the concepts of photogrammetry as well as the camera parameters file, which is not readily set. It is possible to manually alter restricted processing results in Pix4D. As a result, faulty results are not obtained in regions with the same texture, while production points in other areas are of poor quality. When compared to the other three applications, PhUAS fared poorly aesthetically and geometrically. The user must enter many parameters or thresholds in the processing phases. Therefore, the user must be sufficiently informed of the specifics of photogrammetric and machine vision algorithms to understand that the quality of software output is largely reliant on these factors. Furthermore, check point findings revealed that theRMSE in AgisMesh, UASmas, Pix4D, and PhUAS software packages was 2.82, 2.63, 5.84, and 3.03 cm, respectively. Furthermore, quantitative findings obtained by picking random points revealed that UASmas has an accuracy of 3.51 cm, PhUAS has 10.45 cm, and Pix4D was 6.87 cm more accurate than AgisMesh in three residential, barren, and green space regions. Taking into account all of the benefits and evaluations of visual and geometric correctness, the performance and accuracy of AgisMesh, UASmas, Pix4D, and PhUAS may be ranked from one to four, accordingly.