Robust Scale-Invariant Feature Matching for Remote Sensing Image Registration

When the scale-invariant feature transform (SIFT) is adopted in the registration of remote sensing images, a lot of incorrect matches of keypoints will appear owing to the significant difference in the image intensity between remote sensing images compared to visible images. Scale-orientation joint...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2009-04, Vol.6 (2), p.287-291
Main Authors: Qiaoliang Li, Qiaoliang Li, Guoyou Wang, Guoyou Wang, Jianguo Liu, Jianguo Liu, Shaobo Chen, Shaobo Chen
Format: Article
Language:English
Subjects:
Constrictions
Criteria
Feature extraction
Feature matching
Image analysis
Image fusion
Image registration
Image sensors
Matching
Mutual information
Orientation
Remote sensing
Robustness
Satellites
scale-invariant feature transform (SIFT)
scale-orientation joint restriction criteria
Surveillance
Transforms
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Summary:When the scale-invariant feature transform (SIFT) is adopted in the registration of remote sensing images, a lot of incorrect matches of keypoints will appear owing to the significant difference in the image intensity between remote sensing images compared to visible images. Scale-orientation joint restriction criteria are proposed to achieve robust feature matching for keypoints in remote sensing images. Moreover, the feature descriptor of each keypoint is also refined to overcome the difference in the gradient intensity and orientation between remote image pairs. Experimental results for multidate, multispectral, and multisensor remote images indicate that the proposed method improves the match performance compared to intensity- and SIFT-based methods in terms of correct-match rate and aligning accuracy.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2008.2011751