Accuracy Analysis of UAV Data Processing Using DPW

Choi, Yun Woong; You, Ji Ho; Cho, Gi Sung

doi:10.7319/kogsis.2015.23.4.003

Cited by 8 publications

(5 citation statements)

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“…Initial EOPs are provided by navigation sensors installed in the UAV. They are refined through bundle adjustment processes [7,8]. These techniques are also included in most commercial UAV image processing software.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Image–Selection Algorithm for Large-Scale Stereoscopic Mapping of UAV Images

Lim

Rhee²,

Seo

et al. 2021

Remote Sensing

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Recently, the mapping industry has been focusing on the possibility of large-scale mapping from unmanned aerial vehicles (UAVs) owing to advantages such as easy operation and cost reduction. In order to produce large-scale maps from UAV images, it is important to obtain precise orientation parameters as well as analyzing the sharpness of they themselves measured through image analysis. For this, various techniques have been developed and are included in most of the commercial UAV image processing software. For mapping, it is equally important to select images that can cover a region of interest (ROI) with the fewest possible images. Otherwise, to map the ROI, one may have to handle too many images, and commercial software does not provide information needed to select images, nor does it explicitly explain how to select images for mapping. For these reasons, stereo mapping of UAV images in particular is time consuming and costly. In order to solve these problems, this study proposes a method to select images intelligently. We can select a minimum number of image pairs to cover the ROI with the fewest possible images. We can also select optimal image pairs to cover the ROI with the most accurate stereo pairs. We group images by strips and generate the initial image pairs. We then apply an intelligent scheme to iteratively select optimal image pairs from the start to the end of an image strip. According to the results of the experiment, the number of images selected is greatly reduced by applying the proposed optimal image–composition algorithm. The selected image pairs produce a dense 3D point cloud over the ROI without any holes. For stereoscopic plotting, the selected image pairs were map the ROI successfully on a digital photogrammetric workstation (DPW) and a digital map covering the ROI is generated. The proposed method should contribute to time and cost reductions in UAV mapping.

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Section: Introductionmentioning

confidence: 99%

An Optimal Image–Selection Algorithm for Large-Scale Stereoscopic Mapping of UAV Images

Lim

Rhee²,

Seo

et al. 2021

Remote Sensing

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show abstract

“…For this reason, stereo plotting of UAV images in particular is time consuming and costly. Previous research on large-scale mapping using UAVs dealt with mapping accuracy analysis [11], accuracy in 3D model extraction [12][13][14], and image mosaicking [15][16][17][18][19]. On the other hand, studies on reducing the number of images for mapping or stereoscopic plotting were difficult to find.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Image–Selection Algorithm for Large-Scale Stereoscopic Mapping of UAV Images

Lim¹,

Seo²,

Kim³

et al. 2021

Preprint

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Recently, the mapping industry has been focusing on the possibility of large-scale mapping from unmanned aerial vehicles (UAVs) owing to advantages such as easy operation and cost reduction. In order to produce large-scale maps from UAV images, it is important to obtain precise orientation parameters. For this, various techniques have been developed and are included in most of the commercial UAV image processing software. For mapping, it is equally important to select images that can cover a region of interest (ROI) with the fewest possible images. Otherwise, to map the ROI, one may have to handle too many images, and commercial software does not provide information needed to select images, nor does it explicitly explain how to select images for mapping. For these reasons, stereo mapping of UAV images in particular is time consuming and costly. In order to solve these problems, this study proposes a method to select images intelligently. We can select a minimum number of image pairs to cover the ROI with the fewest possible images. We can also select optimal image pairs to cover the ROI with the most accurate stereo pairs. We group images by strips, and generate the initial image pairs. We then apply an intelligent scheme to iteratively select optimal image pairs from the start to the end of an image strip. According to the results of the experiment, the number of images selected is greatly reduced by applying the proposed optimal image–composition algorithm. The selected image pairs produce a dense 3D point cloud over the ROI without any holes. For stereoscopic plotting, the selected image pairs were map the ROI successfully on a digital photogrammetric workstation (DPW), and a digital map covering the ROI is generated. The proposed method should contribute to time and cost reductions in UAV mapping.

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“…. 또한, 소프트웨어를 활용하여 대량의 항공사진을 분석하여 DEM과 DSM을 구축 하고 지형측량과 비교하여 정확도의 결과를 비교하였다 (Choi et al, 2015;Rhee et al, 2015;Coveney and Roberts, 2017 (Goncalves and Henriques, 2015;Pérez-Alberti and Trenhaile, 2015;Yu et al, 2016;Son et al, 2017 (Udin and Ahmad, 2014;Rhee el al., 2015;Mesas-Carrascosa et al, 2016;Agüera-Vega et al, 2017;Kim and Yoon, 2018;Park and Um, 2018). 지상 기준점을 고려한 비선형 방법을 개발하여 실좌표 영상으로 변화하는 방법도 제안되었다 (Noh et al, 2011…”

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A Study on 3D Model Construction by Controlling Unmanned Aerial Vehicle of Camera Angle

Song¹,

Lee²,

Kim³

et al. 2021

J. Korean Soc. Hazard Mitig

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In this study, the camera angle of a UAV (Unmanned Aerial Vehicle) was adjusted to take aerial photographs, and 2D and 3D models were constructed to evaluate the quantitative impact. The study area was Waryong Bridge, located in Dalseong-gun, Daegu Metropolitan City. The camera angles of the UAV were 90°, 75°, and 60°; DSM, orthophoto, and the 3D model were analyzed. As a result of the analysis of DSM and orthophoto, images were 1.05 times in 75° and 1.10 times in 60° compared to 90°, and matching was 1.09 times in 75° and 1.60 times in 60° compared to 90°. The point cloud for building 3D models increased analysis points to 1.17 times for 75° and 1.47 times for 60° compared to 90°. However, the area of an orthophoto of 1 pixel increased by 1.10 times for 75° and 1.34 times for 60° compared to 90°, resulting in a decrease in resolution. As the camera angle of the UAV decreases, the overlapping ratio of aerial photographs increases and the structure of wide areas can be implemented. However, since a large difference occurs in the precision of the 3D model, a shooting plan according to the purpose should be established.

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Accuracy Analysis of UAV Data Processing Using DPW

Cited by 8 publications

References 0 publications

An Optimal Image–Selection Algorithm for Large-Scale Stereoscopic Mapping of UAV Images

An Optimal Image–Selection Algorithm for Large-Scale Stereoscopic Mapping of UAV Images

An Optimal Image–Selection Algorithm for Large-Scale Stereoscopic Mapping of UAV Images

A Study on 3D Model Construction by Controlling Unmanned Aerial Vehicle of Camera Angle

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