Multi-Variant Accuracy Evaluation of UAV Imaging Surveys: A Case Study on Investment Area

Abstract

The main focus of the presented study is a multi-variant accuracy assessment of a photogrammetric 2D and 3D data collection, whose accuracy meets the appropriate technical requirements, based on the block of 858 digital images (4.6 cm ground sample distance) acquired by Trimble® UX5 unmanned aircraft system equipped with Sony NEX-5T compact system camera. All 1418 well-defined ground control and check points were a posteriori measured applying Global Navigation Satellite Systems (GNSS) using the real-time network method. High accuracy of photogrammetric products was obtained by the computations performed according to the proposed methodology, which assumes multi-variant images processing and extended error analysis. The detection of blurred images was preprocessed applying Laplacian operator and Fourier transform implemented in Python using the Open Source Computer Vision library. The data collection was performed in Pix4Dmapper suite supported by additional software: in the bundle block adjustment (results verified using RealityCapure and PhotoScan applications), on the digital surface model (CloudCompare), and georeferenced orthomosaic in GeoTIFF format (AutoCAD Civil 3D). The study proved the high accuracy and significant statistical reliability of unmanned aerial vehicle (UAV) imaging 2D and 3D surveys. The accuracy fulfills Polish and US technical requirements of planimetric and vertical accuracy (root mean square error less than or equal to 0.10 m and 0.05 m).

Determining optimal photogrammetric adjustment of images obtained from a fixed‐wing UAV

“…The accuracy criteria of 2 GSD for XY and 3 GSD for Z have also a practical aspect when the optimalisation of image acquisition is taken into consideration for GSD levels of 2.5 to 3.5 cm. Such criteria generally fulfil official accuracy requirements of direct measurement of basic mapping (MSWiA, 2011; ASPRS, 2015; Gabara and Sawicki, 2019). These national requirements do not differ much and the criteria of 2 GSD for XY and 3 GSD for Z are additionally justified when the GSD is of the size in the presented research.…”

Impact of UAV Surveying Parameters on Mixed Urban Landuse Surface Modelling

“…As a statistical parameter, the RMSE values are described, which are interpreted as a "square root of the average of the set of squared differences between dataset coordinate values and coordinate values from an independent source of higher accuracy for identical points" [26]. In this study, check points based RMSEr (horizontal accuracy) and RMSEz (vertical accuracy) of all 16 photogrammetric projects were calculated and compared with ASPRS accuracy standards as per the following formulas:…”

“…Seifert et al [25] sampled for images at varying frequencies to obtain forward overlap ratios ranging between 91% and 99% for forest mapping through UAV captured videos. Gabara and Sawicki [26] assessed the multivariate accuracy of UAV surveys conducted with 85% and 65% forward overlap and 85%, 65%, and 45% side overlap. The study was conducted to evaluate the impact of less forward and side overlap and the reduction of image number on the significant decrease of the bundle block adjustment (BBA) accuracy.…”

Quality Evaluation of 3d Building Models Based on Low-Altitude Imagery and Airborne Laser Scanning Point Clouds

“…The photogrammetric 3D modelling and reconstruction of scenes and objects in a varied spectrum of applications are currently performed based on laser scanning (Oude Elberink and Vosselman, 2011;Rutzinger et al, 2009;Pilarska et al, 2017;Li et al, 2019) or image data (Remondino et al, 2011;Remondino et al, 2014;Dahlke et al, 2015;Gabara and Sawicki, 2019a). Image-based dense point clouds are generated using dense image matching.…”