Robust Mosaicking of Lightweight UAV Images Using Hybrid Image Transformation Modeling

Kim, Jae-In; Kim, Hyuncheol; Kim, Taejung

doi:10.3390/rs12061002

Cited by 7 publications

(5 citation statements)

References 33 publications

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“…The Y-parallax is a vertical coordinate difference between left and right tie points mapped from an origin image to an epipolar image through epipolar resampling. The closer the Y-parallax is to zero, the higher the accuracy [17]. Next, optimal image pairs are determined by an iterative pair-selection process.…”

Section: Proposed Methodsmentioning

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: Proposed Methodsmentioning

confidence: 99%

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

Lim

Rhee²,

Seo

et al. 2021

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Once images are grouped into strips, overlaps between adjacent images within the same strip are estimated. First, the ground coverage of individual images is calculated by projecting image corner points onto the ground reference plane though the collinear equations in (3) and ( 4 Once individual image coverages are calculated, an overlapping area between two images is calculated based on the ground reference plane (Figure 3) [21][22][23]. As shown in Figure 3, the coverage of the reference image and that of the target image define the reference polygon and target polygon, respectively.…”

Section: Initial Image Pair Compositionmentioning

confidence: 99%

“…One may also impose stereoscopic quality measures for initial image-pair selection. Yparallax errors among image pairs are good candidates for measurement [13,21,22], and can help explain how well two images will align after epipolar rectification. Convergence angles are also good candidates.…”

Section: Figure 3 An Overlap Area In Ground Spacementioning

confidence: 99%

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

Lim¹,

Seo²,

Kim³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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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“…Despite their advantages, the limited field of view of UAVs necessitates the acquisition of numerous images to cover an entire area of interest. To enhance the efficiency of UAV remote sensing, rapid stitching of multiple UAV images into a seamless mosaic image is essential (Kim et al, 2020). However, relief displacement introduces variations in object appearance across images, causing mismatch errors at the junction regions on the mosaic image (Yuan et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Triangulated Irregular Network based Seamline Determination for Fast Image Stitching of Multiple UAV Images

Yoon,

Kim

2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. To maintain the efficiency of UAV (unmanned aerial vehicle) remote sensing, rapid image stitching is essential to make multiple UAV images into a seamless mosaic image. Relief displacement introduces variations in object appearance for each image, causing mismatch errors at mosaic seamlines. Traditional approaches involve orthorectifying images using DSMs (digital surface models). While these approaches allow for accurate image stitching, they do not cope with the advantages of UAVs due to their time consumption. In contrast, fast image stitching techniques that do not use orthorectification are well suited for UAV image processing. Related researches have attempted to optimize seamlines to eliminate the errors caused by relief displacement without the use of DSMs. We propose to utilize a TIN (triangular irregular network) of tiepoints to effectively eliminate errors caused by relief displacement while maintaining the fast speed of image stitching. In this study, a TIN is constructed based image tiepoints whose ground coordinates have been obtained through bundle adjustment. The edges of the TIN are used to generate seamlines for image stitching, and the facets of the TIN are used to select minimal images for image stitching and to optimize seamlines. Image stitching results of our proposed method had small error of 1–2 pixels and the processing time of less than 10 minutes for 97 UAV images. This study showed that the proposed method could stitch multiple images while maintaining stable quality using only geometric clues of a TIN of tiepoints.

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Robust Mosaicking of Lightweight UAV Images Using Hybrid Image Transformation Modeling

Cited by 7 publications

References 33 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

Triangulated Irregular Network based Seamline Determination for Fast Image Stitching of Multiple UAV Images

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