Paper Info
Title
Multispectral Image Alignment With Nonlinear Scale-Invariant Keypoint and Enhanced Local Feature Matrix
Abstract
The scale space-based method has been recently studied for multispectral alignment; however, due to the significant intensity difference between the image pairs, there are usually not enough keypoint correspondences found, and the robustness of the alignment tends to be compromised. In this letter, we attempt to improve the performance from the following two aspects: 1) to avoid the boundary blurring of Gaussian scale space, we adopt nonlinear scale space to explore more keypoints with potential of being correctly matched, and 2) a robust feature descriptor is proposed, and the resulting feature matrix is matched using the previously proposed rotation-invariant distance to obtain more correct keypoint correspondences. Experimental results for multispectral remote images indicate that the proposed method improves the matching performance compared to state-of-the-art methods in terms of correctly matched number of keypoints, aligning accuracy, and rate of correctly matched image pairs. It is also revealed in this letter that, if the descriptor is carefully designed, the local features are distinctive enough for produce good matching even when the main orientation is not present.
Year
DOI
Venue
2015
10.1109/LGRS.2015.2412955
IEEE Geoscience and Remote Sensing Letters
Keywords
Field
DocType
REGISTRATION,DIFFUSION
Computer vision,Scale invariance,Feature detection (computer vision),Pattern recognition,Feature (computer vision),Multispectral image,Scale space,Robustness (computer science),Gaussian,Multispectral pattern recognition,Artificial intelligence,Mathematics
Journal
Volume
Issue
ISSN
PP
99
1545-598X
Citations 
PageRank 
References 
5
0.46
12
Authors
6
Name
Order
Citations
PageRank
Li, Q.1784.31
Suwen Qi2112.40
Yuanyuan Shen3426.50
Dong Ni436737.37
Hui-sheng Zhang515924.84
tianfu wang6554.82