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Robust Scale-Invariant Feature Matching for Remote Sensing Image Registration

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4 Author(s)
Qiaoliang Li ; Inst. for Pattern Recognition & Artificial Intell., Huazhong Univ. of Sci. & Technol., Wuhan ; Guoyou Wang ; Jianguo Liu ; Shaobo Chen

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.

Published in:

Geoscience and Remote Sensing Letters, IEEE  (Volume:6 ,  Issue: 2 )

Date of Publication:

April 2009

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