Multilevel SIFT Matching for Large-Size VHR Image Registration
A fast approach is proposed in this letter for large-size very high resolution image registration, which is accomplished based on coarse-to-fine strategy and blockwise scale-invariant feature transform (SIFT) matching. Coarse registration is implemented at low resolution level, which provides a geom...
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| Veröffentlicht in: | IEEE geoscience and remote sensing letters 2012-03, Vol.9 (2), p.171-175 |
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| container_title | IEEE geoscience and remote sensing letters |
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| creator | Huo, Chunlei Pan, Chunhong Huo, Leigang Zhou, Zhixin |
| description | A fast approach is proposed in this letter for large-size very high resolution image registration, which is accomplished based on coarse-to-fine strategy and blockwise scale-invariant feature transform (SIFT) matching. Coarse registration is implemented at low resolution level, which provides a geometric constraint. The constraint makes the blockwise SIFT matching possible and is helpful for getting more matched keypoints at the latter refined procedure. Refined registration is achieved by blockwise SIFT matching and global optimization on the whole matched keypoints based on iterative reweighted least squares. To improve the efficiency, blockwise SIFT matching is implemented in a parallel manner. Experiments demonstrate the effectiveness of the proposed approach. |
| doi_str_mv | 10.1109/LGRS.2011.2163491 |
| format | Article |
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| ispartof | IEEE geoscience and remote sensing letters, 2012-03, Vol.9 (2), p.171-175 |
| issn | 1545-598X 1558-0571 |
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| source | IEEE Electronic Library (IEL) |
| subjects | Accuracy Blocking Coarse-to-fine strategy Feature extraction geometric constraint Geometric constraints Image registration large-size image registration Least squares method Matching Multilevel Optimization parallel-based architecture Remote sensing Satellites Spatial resolution Strategy |
| title | Multilevel SIFT Matching for Large-Size VHR Image Registration |
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