Efficient seamline determination for UAV image mosaicking using edge detection

Nguyen, Truong Linh; Byun, Young-Gi; Han, Derick; Huh, Jungwon

doi:10.1080/2150704x.2018.1475772

Cited by 6 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ref. [23] developed a traditional frame-to-frame algorithm to mosaic UAV images. They used the edge detection method to limit the seamline along the roadways.…”

Section: Viewing Angle Disparitiesmentioning

confidence: 99%

“…To avoid this problem, seamlines undergo an optimization process to find the best seamlines that pass over the minimum number of high-rise objects possible [21]. To optimize the seamlines, the existing optimization methods can be grouped into frame-to-frame (local) [22][23][24] and multi-frame (global) [25][26][27]. Frame-to-frame methods determine the optimized seamlines image by image, whereas in the multi-frame approaches this is done simultaneously.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Varshosaz,

Sajadian,

Pirasteh

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

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.

show abstract

“…Ref. [23] developed a traditional frame-to-frame algorithm to mosaic UAV images. They used the edge detection method to limit the seamline along the roadways.…”

Section: Viewing Angle Disparitiesmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…The most representative feature extraction algorithms, such as scale-invariant feature transform (SIFT) [59] and speeded-up robust features (SURF) [60], are mostly utilized in feature-based frameworks. In addition, many excellent algorithms for image mosaicking exist, which generally consists of radiometric normalization [61][62][63][64][65], seam line detection [66][67][68] and image blending [14,[69][70][71]. However, image registration, radiometric normalization, and seam line detection are still challenging tasks for the mosaicking of high-resolution remote sensing images.…”

Section: Related Workmentioning

confidence: 99%

Generating High-Quality and High-Resolution Seamless Satellite Imagery for Large-Scale Urban Regions

Feng

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Urban geographical maps are important to urban planning, urban construction, land-use studies, disaster control and relief, touring and sightseeing, and so on. Satellite remote sensing images are the most important data source for urban geographical maps. However, for optical satellite remote sensing images with high spatial resolution, certain inevitable factors, including cloud, haze, and cloud shadow, severely degrade the image quality. Moreover, the geometrical and radiometric differences amongst multiple high-spatial-resolution images are difficult to eliminate. In this study, we propose a robust and efficient procedure for generating high-resolution and high-quality seamless satellite imagery for large-scale urban regions. This procedure consists of image registration, cloud detection, thin/thick cloud removal, pansharpening, and mosaicking processes. Methodologically, a spatially adaptive method considering the variation of atmospheric scattering, and a stepwise replacement method based on local moment matching are proposed for removing thin and thick clouds, respectively. The effectiveness is demonstrated by a successful case of generating a 0.91-m-resolution image of the main city zone in Nanning, Guangxi Zhuang Autonomous Region, China, using images obtained from the Chinese Beijing-2 and Gaofen-2 high-resolution satellites.

show abstract

Image Stitching using AKAZE Features

Sharma

Jain

2020

J Indian Soc Remote Sens

View full text Add to dashboard Cite

Efficient seamline determination for UAV image mosaicking using edge detection

Cited by 6 publications

References 9 publications

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

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

Generating High-Quality and High-Resolution Seamless Satellite Imagery for Large-Scale Urban Regions

Image Stitching using AKAZE Features

Contact Info

Product

Resources

About