Image Registration Via Marginal Distribution Adaptation

The distribution of feature vectors plays a critical role in image registration. In this letter, we propose a novel approach for remote sensing image registration based on marginal distribution adaptation. First, we map the feature vectors of reference and sensed images into a latent space. Transfer...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2022, Vol.19, p.1-5
Main Authors: Yan, Xiaohu, Yang, Jinfeng, Liu, Xiaohua, He, Fazhi, Sun, Hao
Format: Article
Language:English
Subjects:
Adaptation
Algorithms
Distribution
Distribution adaptation
Expression vectors
Feature extraction
feature matching
Image registration
Kernel
marginal distribution
Mathematical analysis
Power capacitors
Registration
Remote sensing
Runtime
Software algorithms
Vectors
Vectors (mathematics)
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Summary:The distribution of feature vectors plays a critical role in image registration. In this letter, we propose a novel approach for remote sensing image registration based on marginal distribution adaptation. First, we map the feature vectors of reference and sensed images into a latent space. Transfer component analysis (TCA) is employed to compute the transformation matrix by minimizing the maximum mean discrepancy (MMD). Then, we match feature vectors in the latent space where their marginal distributions are similar, which can increase correct correspondences and enhance registration accuracy. Finally, we test the proposed algorithm on ten real image pairs. The effectiveness and efficiency of our approach are verified by experimental results.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2021.3091592