Mosaicking of Unmanned Aerial Vehicle Imagery in the Absence of Camera Poses

Xu, Yaming; Ou, Jianliang; He, Hu; Zhang, Xiaohu; Mills, J. P.

doi:10.3390/rs8030204

Cited by 59 publications

(31 citation statements)

References 21 publications

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“…-UAV image mosaicking data set [136]: This data set consists of hundreds of images captured by the UAV. The corresponding DOMs are generated by DPGrid, which can be used as the golden standard to evaluate your mosaicking algorithms.…”

Section: Item Algorithmsmentioning

confidence: 99%

Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, applications, and prospects

Xiang

Xia

Zhang

2019

IEEE Geosci. Remote Sens. Mag.

163

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The past few decades have witnessed the great progress of unmanned aircraft vehicles (UAVs) in civilian fields, especially in photogrammetry and remote sensing. In contrast with the platforms of manned aircraft and satellite, the UAV platform holds many promising characteristics: flexibility, efficiency, highspatial/temporal resolution, low cost, easy operation, etc., which make it an effective complement to other remote-sensing platforms and a cost-effective means for remote sensing. Considering the popularity and expansion of UAV-based remote sensing in recent years, this paper provides a systematic survey on the recent advances and future prospectives of UAVs in the remote-sensing community. Specifically, the main challenges and key technologies of remote-sensing data processing based on UAVs are discussed and summarized firstly. Then, we provide an overview of the widespread applications of UAVs in remote sensing. Finally, some prospects for future work are discussed. We hope this paper will provide remotesensing researchers an overall picture of recent UAV-based remote sensing developments and help guide the further research on this topic.

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Section: Item Algorithmsmentioning

confidence: 99%

Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, applications, and prospects

Xiang

Xia

Zhang

2019

IEEE Geosci. Remote Sens. Mag.

163

View full text Add to dashboard Cite

show abstract

“…Aicardi et al [32] adopts an image-based approach for co-registering multi-temporal UAV image datasets; however, it only estimates the relative transformation between the epochs, while the absolute transformation of the epoch is not solved. Finally, Xu et al [33] presents an fast and efficient way for UAV image mosaicking without the explicit computation of camera poses; however, the image mosaics are not geo-registered.…”

Section: Related Workmentioning

confidence: 99%

Automatic UAV Image Geo-Registration by Matching UAV Images to Georeferenced Image Data

et al. 2017

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Recent years have witnessed the fast development of UAVs (unmanned aerial vehicles).As an alternative to traditional image acquisition methods, UAVs bridge the gap between terrestrial and airborne photogrammetry and enable flexible acquisition of high resolution images. However, the georeferencing accuracy of UAVs is still limited by the low-performance on-board GNSS and INS. This paper investigates automatic geo-registration of an individual UAV image or UAV image blocks by matching the UAV image(s) with a previously taken georeferenced image, such as an individual aerial or satellite image with a height map attached or an aerial orthophoto with a DSM (digital surface model) attached. As the biggest challenge for matching UAV and aerial images is in the large differences in scale and rotation, we propose a novel feature matching method for nadir or slightly tilted images. The method is comprised of a dense feature detection scheme, a one-to-many matching strategy and a global geometric verification scheme. The proposed method is able to find thousands of valid matches in cases where SIFT and ASIFT fail. Those matches can be used to geo-register the whole UAV image block towards the reference image data. When the reference images offer high georeferencing accuracy, the UAV images can also be geolocalized in a global coordinate system. A series of experiments involving different scenarios was conducted to validate the proposed method. The results demonstrate that our approach achieves not only decimeter-level registration accuracy, but also comparable global accuracy as the reference images.

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“…In addition, the UAV pre-processing procedures, i.e. image stitching (Li, Hui et al 2015;Xu et al 2016; and 3D reconstruction (Turner, Lucieer, and Christopher 2012;Schönberger, Fraundorfer, and Frahm 2014), output extremely large-scale Digital Orthophoto Map (panorama or DOM) with VHR. The panorama or DOM can provide a global and detailed inspection of the investigative area.…”

Section: Motivation and Objectivementioning

confidence: 99%

Analysis of large-scale UAV images using a multi-scale hierarchical representation

Wang

Bai

et al. 2018

Geo-spatial Information Science

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c state Key laboratory for information engineering in surveying, mapping and remote sensing (liesmars), Wuhan University, Wuhan, china ABSTRACT Unmanned aerial vehicle (UAV)-based imaging systems have many superiorities compared with other platforms, such as high flexibility and low cost in collecting images, providing wide application prospects. However, the acquisition of the UAV-based image commonly results in very high resolution and very large-scale images, which poses great challenges for subsequent applications. Therefore, an efficient representation of large-scale UAV images is necessary for the extraction of the required information in a reasonable time. In this work, we proposed a multi-scale hierarchical representation, i.e. binary partition tree, for analyzing large-scale UAV images. More precisely, we first obtained an initial partition of images by an oversegmentation algorithm, i.e. the simple linear iterative clustering. Next, we merged the similar superpixels to build an object-based hierarchical structure by fully considering the spectral and spatial information of the superpixels and their topological relationships. Moreover, objects of interest and optimal segmentation were obtained using object-based analysis methods with the hierarchical structure. Experimental results on processing the post-seismic UAV images of the 2013 Ya'an earthquake and the mosaic of images in the South-west of Munich demonstrate the effectiveness and efficiency of our proposed method.

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Mosaicking of Unmanned Aerial Vehicle Imagery in the Absence of Camera Poses

Cited by 59 publications

References 21 publications

Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, applications, and prospects

Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, applications, and prospects

Automatic UAV Image Geo-Registration by Matching UAV Images to Georeferenced Image Data

Analysis of large-scale UAV images using a multi-scale hierarchical representation

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