High-Precise True Digital Orthoimage Generation and Accuracy Assessment based on UAV Images

Shoab, Mohammad; Singh, Vivek K.; Ravibabu, Mandla V.

doi:10.1007/s12524-021-01364-z

Cited by 13 publications

(5 citation statements)

References 21 publications

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“…To construct this output, it is necessary to use a DEM to define the orthoimage matrix, where each pixel corresponds to an element of the ground plane. In order to improve the final result and eliminate projection differences and preserve the correct positions of objects and terrain features (Lamsters et al, 2020), it is possible to process a "True Digital Orthoimage" using a DSM instead of a DEM and ensuring the geometric accuracy of features and visibility of features (Shoab et al, 2022).…”

Section: Digital Model and Orthophoto Processingmentioning

confidence: 99%

Uav Photogrammetry as a Multidisciplinary Approach in Engineering Design and Sustainable Land Management

ALFIO

2024

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Section: Digital Model and Orthophoto Processingmentioning

confidence: 99%

Uav Photogrammetry as a Multidisciplinary Approach in Engineering Design and Sustainable Land Management

ALFIO

2024

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“…The generation of orthophoto mosaics using drone images is an important application that has attracted a lot of interest in recent years [1][2][3][4][5][6][7]. To create an orthophoto mosaic from a set of images, individual orthophotos are initially produced through orthorectification using direct or indirect methods [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Automated Two-Step Seamline Detection for Generating Large-Scale Orthophoto Mosaics from Drone Images

Varshosaz,

Sajadian,

Pirasteh

et al. 2024

Remote Sensing

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To generate an orthophoto mosaic from a collection of aerial images, the original images are first orthorectified individually using a Digital Surface Model (DSM). Then, they are stitched together along some determined seamlines to form the orthophoto mosaic. Determining appropriate seamlines is a critical process, as it affects the visual and geometric quality of the results. The stitching process can usually be done in frame-to-frame or multi-frame modes. Although the latter is more efficient, both still involve a lot of pre-processing, such as creating individual orthophotos, image registration, and overlap extraction. This paper presents a novel coarse-to-fine approach that directly determines the seamline network without such pre-processing. Our method has been specifically applied for UAV photogrammetry projects where, due to the large number of images and the corresponding overlaps, the orthophoto mosaic generation can be very challenging and time-consuming. We established the seamlines simultaneously for all the images through a two-step process. First, a DSM was generated, and a low-resolution grid was overlayed. Then, for each grid point, an optimal image was selected. Then, the grid cells are grouped into polygons based on their corresponding optimal image. Boundaries of these polygons established our seamline network. Thereafter, to generate the orthophoto mosaic, we overlayed a higher/full resolution grid on the top of the DSM, the optimal image of each point of which was quickly identified via our low-resolution polygons. In this approach, not only seamlines were automatically generated, but also were the need for the creation, registration, and overlap extraction of individual orthophotos. Our method was systematically compared with a conventional frame-to-frame (CF) technique from different aspects, including the number of double-mapped areas, discontinuities across the seamlines network, and the amount of processing time. The outcomes revealed a 46% decrease in orthophoto generation time and a notable reduction in the number of double-mapped areas, sawtooth effects, and object discontinuities within the constructed orthophoto mosaic.

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“…This technique orthorectifies individual images using a terrain model. The orthorectified images are then combined into a single orthoimage through image rearrangement and image mosaicking [8][9][10]. Terrain-based techniques can produce highly accurate mosaics because they project images by recreating actual imaging and terrain geometry.…”

Section: Introductionmentioning

confidence: 99%

Seamline Optimization Based on Triangulated Irregular Network of Tiepoints for Fast UAV Image Mosaicking

Yoon,

Kim

2024

Remote Sensing

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UAV remote sensing is suitable for urgent image monitoring and periodic observation of an area of interest. To observe a target area using UAVs, many images must be acquired because of the narrow image coverage of UAVs. To increase the efficiency of UAV remote sensing, UAV mosaicking is used to create a single image from multiple UAV images. In order to maintain the strength of rapid UAV deployment, UAV mosaicked images have to be quickly generated through image-based mosaicking techniques. In addition, it is necessary to improve the mosaic errors of image-based techniques that often occur in contrast to terrain-based techniques. Relief displacement is a major source of mosaic error and can be detected by utilizing a terrain model. We have proposed an image-based mosaicking technique utilizing TIN, which is a model that can represent terrain with discontinuously acquired height information of ground points. Although the TIN is less accurate than DSM, it is simpler and faster to utilize for image mosaicking. In our previous work, we demonstrated fast processing speed of mosaicking using TIN-based image tiepoints. In this study, we improve the quality of image-based mosaicking techniques by optimizing seamline-based TIN geometry. Three datasets containing buildings with large relief displacement were used in this study. The experiment results showed that the TIN based on the proposed method improved the mosaic error caused by relief displacement significantly.

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High-Precise True Digital Orthoimage Generation and Accuracy Assessment based on UAV Images

Cited by 13 publications

References 21 publications

Uav Photogrammetry as a Multidisciplinary Approach in Engineering Design and Sustainable Land Management

Uav Photogrammetry as a Multidisciplinary Approach in Engineering Design and Sustainable Land Management

Automated Two-Step Seamline Detection for Generating Large-Scale Orthophoto Mosaics from Drone Images

Seamline Optimization Based on Triangulated Irregular Network of Tiepoints for Fast UAV Image Mosaicking

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