2017
DOI: 10.3390/rs9080813
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On-Board GNSS/IMU Assisted Feature Extraction and Matching for Oblique UAV Images

Abstract: Feature extraction and matching is a crucial task in the fields of computer vision and photogrammetry. Even though wide researches have been reported, some issues are still existing for oblique images. This paper exploits the use of on-board GNSS/IMU (Global Navigation Satellite System/Inertial Measurement Unit) data to achieve efficient and reliable feature extraction and matching for oblique unmanned aerial vehicle (UAV) images. Firstly, rough POS (Positioning and Orientation System) is calculated for each i… Show more

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Cited by 41 publications
(37 citation statements)
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References 29 publications
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“…local homography constraint, can be used to guide the propagative matching [74]. The other is to apply geometric rectification before image matching [40]. If images collected by UAVs contain rough or precise exterior orientation elements and camera installation parameters, they can be used for geometric rectification of oblique UAV images to relieve perspective deformations.…”
Section: Low-altitude Uav Image Matchingmentioning
confidence: 99%
See 1 more Smart Citation
“…local homography constraint, can be used to guide the propagative matching [74]. The other is to apply geometric rectification before image matching [40]. If images collected by UAVs contain rough or precise exterior orientation elements and camera installation parameters, they can be used for geometric rectification of oblique UAV images to relieve perspective deformations.…”
Section: Low-altitude Uav Image Matchingmentioning
confidence: 99%
“…To obtain the matching pairs as evenly distributed as possible, the divide-and-conquer and the tiling strategy are often adopted [40]. Images are split into blocks, and features are extracted and matched from the corresponding blocks.…”
Section: Low-altitude Uav Image Matchingmentioning
confidence: 99%
“…Second, the rough pose of each image can be calculated by using camera mounting angles, because they are the approximate respective displacements between UAV platforms and oblique cameras. The detailed process for image pose calculation is presented in Jiang and Jiang (2017b). By using the computed image poses, ground coverage of images for the dataset is illustrated in Figure 4, which are the projection of image corners on the average elevation plane of the test site.…”
Section: Datasetmentioning
confidence: 99%
“…Initial photogrammetric processing of these multiview angle images through aerial triangulation approaches produced unsatisfactory results, due to the complexity of oblique images, which contain vastly different-perspective viewing angles, large-scale differences within individual images, and occlusion of objects within a scene [40][41][42][43]. Development of unique processing solutions that maintain the rigorous standards of aerial triangulation while capitalizing on the advantages presented by airborne oblique views continues to be a key topic in photogrammetric research [44][45][46].…”
Section: Introductionmentioning
confidence: 99%