Determination of the optimum number and distribution of the ground control points in stereo imaging to achieve precise positions

Abstract

A precise location in aerial surveying can only be achieved using Ground Control Points GCPs. At least three point should be used and as the number increases the model will be more precise in X, Y and Z positions for a certain extent. The distribution of the GCPs also affect the precision of the 3D model resulted from the aerial imaging. This study aims to find the optimum number and distribution of the GCPs to achieve the minimal error in points location. 1.5 km2 of longitudinal area was imaged with a commercial UAV named DJI Mavic 2 pro with at nadir camera orientation at height of 100 m above the ground. A total of 1515 images were taken with average ground sampling distance (GSD) of 2.3 cm. Deferential Global Positioning System DGPS Leica GS 15 receiver were used to observe the 62 ground control points with PPK fashion. The project area was divided into two regions the first region has a parallel distribution of GCPs and the second region has a zigzag distribution. The images were processed using Pix4Dmapper and Agisoft Metashape software by applying a bundle adjustment process with an incremental number of GCPs starting with 3 and finishes with 26 for each distribution pattern, the remaining points were used as a check points to determine the precision of the model at each trial. The resulted coordinates of check points were compared with the coordinates observed with the DGPS. The comparison depicts the optimum number of GCPs required for the best location precision and the best distribution pattern.

Optimising UAV Data Acquisition and Processing for Photogrammetry: A Review

“…Increasing the number of GCPs to 18, very similar RMSE values (XY 0.027 and Z 0.055 m) were obtained. The alternating and parallel distribution of GCPs was also compared in [76]. The results confirm that alternating distribution gives better results, especially for a smaller number of GCPs.…”

The evaluation of carbon monoxide emissions using regression analysis and GIS techniques

“…The result showed that CO concentrations were higher than other pollutants such as (PM2.5, and NO2). In another study, Gaar et al, 2009 [21] applied A Gaussian plume model to predict the dispersion of CO emissions of elevated flares in Basra city, their results indicated that the highest CO concentrations occur under unstable weather conditions. In general, dispersion models can be consistent with the GIS environment, another technique depends on remote sensing data such as satellite images, several studies in many applications used satellite images [21][22][23][24][25][26][27][28][29][30][31] Rajab et al, 2013 measured carbon monoxide concentrations at national level in Iraq, their method has utilized satellite data captured by Terra spacecraft with a spatial resolution (1˚x1˚), the results showed that there was a notable increase in CO concentrations over industrial and congested cities compared to rural and desert areas.…”

“…In another study, Gaar et al, 2009 [21] applied A Gaussian plume model to predict the dispersion of CO emissions of elevated flares in Basra city, their results indicated that the highest CO concentrations occur under unstable weather conditions. In general, dispersion models can be consistent with the GIS environment, another technique depends on remote sensing data such as satellite images, several studies in many applications used satellite images [21][22][23][24][25][26][27][28][29][30][31] Rajab et al, 2013 measured carbon monoxide concentrations at national level in Iraq, their method has utilized satellite data captured by Terra spacecraft with a spatial resolution (1˚x1˚), the results showed that there was a notable increase in CO concentrations over industrial and congested cities compared to rural and desert areas. In another study, Majeed al et., 2020 [33] used satellite data obtained from NASA's Global Modelling and Assimilation Office to analyse surface CO over Iraq, their long-term prediction indicated that CO concentrations reached the highest values over dense and industrial cities such as Baghdad and Basra, in addition, CO concentrations were negative with wind speed and temperature.…”