UAV Image Mosaicking Based on Multiregion Guided Local Projection Deformation
The goal of unmanned aerial vehicle (UAV) image mosaicking is to create natural- looking mosaics without artifacts due to the parallax of the image and relative camera motion. UAV remote sensing is a low-altitude technology and the UAV imaged scene is not effectively planar, yielding parallax on the...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9130864/ |
| _version_ | 1819150087562985472 |
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| author | Quan Xu Jun Chen Linbo Luo Wenping Gong Yong Wang |
| author_facet | Quan Xu Jun Chen Linbo Luo Wenping Gong Yong Wang |
| author_sort | Quan Xu |
| collection | DOAJ |
| description | The goal of unmanned aerial vehicle (UAV) image mosaicking is to create natural- looking mosaics without artifacts due to the parallax of the image and relative camera motion. UAV remote sensing is a low-altitude technology and the UAV imaged scene is not effectively planar, yielding parallax on the images. Moreover, when an object in 3-D is mapped to an image plane, different surfaces have different projections. These projections vary with the viewpoint in a sequence of UAV images, which causes artifacts near some tall buildings in the stitched images. To solve these problems, we propose a novel stitching method based on multiregion guided local projection deformation, which can significantly reduce ghosting due to these projections vary with the viewpoint and the parallax. In the proposed method, the image is initially meshed and each cell corresponds to a local homography for image matching, which can reduce misalignment artifacts in the results compared with 2-D projective transforms or global homography. Then, we divide the overlapping regions of input images into multiple regions by classifying feature points. The partitioned regions which serve well scene constraints, are employed to guide the calculation of local homography. Specifically, instead of calculating local homography by the distance between all the feature points in the image and the vertices of the grid, we propose a strategy where multiple regions have different weights for calculating local homography, which can significantly reduce ghosting near some tall buildings. The benefits of the proposed approach are demonstrated using a variety of challenging cases. |
| first_indexed | 2024-12-22T14:11:56Z |
| format | Article |
| id | doaj.art-004d106c76574236989975ac71a7189e |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-12-22T14:11:56Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-004d106c76574236989975ac71a7189e2022-12-21T18:23:11ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352020-01-01133844385510.1109/JSTARS.2020.30062899130864UAV Image Mosaicking Based on Multiregion Guided Local Projection DeformationQuan Xu0Jun Chen1https://orcid.org/0000-0001-9005-6849Linbo Luo2Wenping Gong3Yong Wang4https://orcid.org/0000-0002-0954-2856School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, ChinaSchool of Automation, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, China University of Geosciences, Wuhan, ChinaSchool of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, ChinaFaculty of Engineering, China University of Geosciences, Wuhan, ChinaSchool of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, ChinaThe goal of unmanned aerial vehicle (UAV) image mosaicking is to create natural- looking mosaics without artifacts due to the parallax of the image and relative camera motion. UAV remote sensing is a low-altitude technology and the UAV imaged scene is not effectively planar, yielding parallax on the images. Moreover, when an object in 3-D is mapped to an image plane, different surfaces have different projections. These projections vary with the viewpoint in a sequence of UAV images, which causes artifacts near some tall buildings in the stitched images. To solve these problems, we propose a novel stitching method based on multiregion guided local projection deformation, which can significantly reduce ghosting due to these projections vary with the viewpoint and the parallax. In the proposed method, the image is initially meshed and each cell corresponds to a local homography for image matching, which can reduce misalignment artifacts in the results compared with 2-D projective transforms or global homography. Then, we divide the overlapping regions of input images into multiple regions by classifying feature points. The partitioned regions which serve well scene constraints, are employed to guide the calculation of local homography. Specifically, instead of calculating local homography by the distance between all the feature points in the image and the vertices of the grid, we propose a strategy where multiple regions have different weights for calculating local homography, which can significantly reduce ghosting near some tall buildings. The benefits of the proposed approach are demonstrated using a variety of challenging cases.https://ieeexplore.ieee.org/document/9130864/Image matchinglocal projectionmultiple regionsUnmanned aerial vehicle (UAV) image mosaicking |
| spellingShingle | Quan Xu Jun Chen Linbo Luo Wenping Gong Yong Wang UAV Image Mosaicking Based on Multiregion Guided Local Projection Deformation IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Image matching local projection multiple regions Unmanned aerial vehicle (UAV) image mosaicking |
| title | UAV Image Mosaicking Based on Multiregion Guided Local Projection Deformation |
| title_full | UAV Image Mosaicking Based on Multiregion Guided Local Projection Deformation |
| title_fullStr | UAV Image Mosaicking Based on Multiregion Guided Local Projection Deformation |
| title_full_unstemmed | UAV Image Mosaicking Based on Multiregion Guided Local Projection Deformation |
| title_short | UAV Image Mosaicking Based on Multiregion Guided Local Projection Deformation |
| title_sort | uav image mosaicking based on multiregion guided local projection deformation |
| topic | Image matching local projection multiple regions Unmanned aerial vehicle (UAV) image mosaicking |
| url | https://ieeexplore.ieee.org/document/9130864/ |
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