Name
Abstract | ||
|---|---|---|
In this letter, we propose a strategy for unmanned aerial vehicle (UAV) image stitching to generate natural-looking panoramas. Traditional methods using homography to perform alignment cannot account for images with parallax, so they require that the input images should be taken from the same viewpoint or the scene should be near the planar. However, remote sensing images obtained by UAVs usually do not satisfy such an ideal situation, and the stitching results always suffer from artifacts. To overcome these challenges and obtain natural-looking panoramas, a global alignment strategy is proposed to better align the input images. Combined with a shape-preserving warp, the stitching results can achieve better alignment accuracy while maintaining the shape. Meanwhile, locality preserving matching (LPM) is used to eliminate mismatches during feature detection and matching for accurate alignment. In addition, to make the stitching results more natural-looking, we also use multiband blending to eliminate artifacts that may exist in the results due to unmodeled effects. Experiments show that our stitching strategy can effectively improve alignment accuracy and obtain natural-looking results compared to other state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/LGRS.2021.3094977 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS |
Keywords | DocType | Volume |
Shape, Image stitching, Distortion, Unmanned aerial vehicles, Remote sensing, Lapping, Geology, Homography, image stitching, locality preserving matching (LPM), multiband blending, unmanned aerial vehicle (UAV) image | Journal | 19 |
ISSN | Citations | PageRank |
1545-598X | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Donghai Guo | 1 | 0 | 1.01 |
| Jun Chen | 2 | 730 | 94.14 |
| Linbo Luo | 3 | 8 | 2.80 |
| Wenping Gong | 4 | 0 | 3.04 |
| Longsheng Wei | 5 | 0 | 1.69 |