# Quality Analysis of Unmanned Aerial Vehicle Images Using a Resolution Target
###### Abstract  
Unmanned aerial vehicle (UAV) photogrammetry is an emerging means of acquiring high-precision rapid spatial information and data because it is cost-effective and highly efficient. However, securing uniform quality in the results of UAV photogrammetry is difficult due to the use of low-cost navigation devices, non-surveying cameras, and rapid changes in shooting locations depending on the aircraft's behavior. In addition, no specific procedures or guidelines exist for performing quantitative quality tests or certification methods on UAV images. Additionally, test tools for UAV image quality assessment only use the ground sample distance (GSD), often resulting in a reduced image quality compared with that of manned aircraft images. In this study, we performed a modulation transfer function (MTF) analysis using a slanted edge target and a GSD analysis to confirm the necessity of MTF analysis in UAV image quality assessments. In this study, we aimed to address this issue by conducting a modulation transfer function (MTF) analysis using a slanted edge target and a ground sample distance (GSD) analysis. This was carried out to confirm the necessity of MTF analysis in evaluating UAV image quality. Furthermore, we analyzed the impact of flight height and mounted sensors on image quality at different study sites. unmanned aerial vehicle (UAV) photogrammetry; ground sample distance (GSD); modulation transfer function (MTF); image quality +
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shortcomings of the traditional method that uses edge and slanted edge targets. [PERSON] et al. [14] proposed a method that automatically measures the modulation transfer function (MTF) with a high success rate and acceptable accuracy using the Hough transform for detecting straight lines from manned aircraft or satellite images. [PERSON] et al. [15] developed a technique that automatically filters UAV image blurring caused by camera movements induced by strong wind, turbulence, or the operator's sudden movement. The technique enables objective analysis as it automatically detects and removes blurring from UAV images, improves image quality, and reduces time and cost compared to the traditional method based on manual detection by the operator. [PERSON] [16] used the Siemens star to measure spatial resolution in UAV images. The Siemens star facilitates analysis and ensures objectivity in all directions as it is unaffected by flight direction. Additionally, as the Siemens star has a smaller size and smaller dimensions than those of the bar target, which is widely used for manned aircraft images, the Siemens star is an ideal resolution target for the UAV photogrammetry system, which flies at low heights. Likewise, many methods are used for testing UAV image quality, including the MTF, edge response, and GSD analyses. However, the quality of UAV images is lower than that of manned aircraft images, in some cases because the UAV image quality test tool assesses quality using only the GSD analysis, which, unlike the MTF or edge response analyses, does not consider the contrast levels alongside image resolution. In addition, securing uniform quality in the results of UAV photogrammetry is difficult due to the use of low-cost navigation devices, non-surveying cameras, and rapid charges in shooting locations depending on the aircraft's behavior. To address this issue, we aimed to investigate the effect of UAV imaging altitude and the performance of mounting sensors on UAV imaging quality. We also aimed to evaluate the necessity of MTF analysis in evaluating UAV imaging quality. To achieve this, we set up inclined corners and bar targets at the shooting site, as shown in Figure 1, and captured images using four types of UAVs and one type of manned aircraft. We conducted shooting using different mounting sensors at the same shooting altitude, and the same mounting sensors were also used at different shooting heights. Subsequently, we generated the final UAV orthoimages and performed both GSD and MTF analyses on the corresponding orthoimages to draw conclusions.