A Novel Subpixel Phase Correlation Method Using Singular Value Decomposition and Unified Random Sample Consensus

• Published in: IEEE transactions on geoscience and remote sensing Vol. 53; no. 8; pp. 4143 - 4156
• Main Authors: Tong, Xiaohua, Ye, Zhen, Xu, Yusheng, Liu, Shijie, Li, Lingyun, Xie, Huan, Li, Tianpeng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Aliasing and noise; Computer simulation; Correlation; Estimation; Least squares approximations; Locking; Matching; Noise; Optimization; Pixels; Remote sensing; Robustness; singular value decomposition (SVD); subpixel phase correlation; unified random sample consensus; unified random sample consensus; subpixel phase correlation; Aliasing and noise; singular value decomposition (SVD)
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2015.2391999

Subpixel translation estimation using phase correlation is a fundamental task for numerous applications in the remote sensing community. The major drawback of the existing subpixel phase correlation methods lies in their sensitivity to corruption, including aliasing and noise, as well as the poor performance in the case of practical remote sensing data. This paper presents a novel subpixel phase correlation method using singular value decomposition (SVD) and the unified random sample consensus (RANSAC) algorithm. In the proposed method, SVD theoretically converts the translation estimation problem to one dimensions for simplicity and efficiency, and the unified RANSAC algorithm acts as a robust estimator for the line fitting, in this case for the high accuracy, stability, and robustness. The proposed method integrates the advantages of Hoge's method and the RANSAC algorithm and avoids the corresponding shortfalls of the original phase correlation method based only on SVD. A pixel-to-pixel dense matching scheme on the basis of the proposed method is also developed for practical image registration. Experiments with both simulated and real data were carried out to test the proposed method. In the simulated case, the comparative results estimated from the generated synthetic image pairs indicate that the proposed method outperforms the other existing methods in the presence of both aliasing and noise, in both accuracy and robustness. Moreover, the pixel locking effect that commonly occurs in subpixel matching was also investigated. The degree of pixel locking effect was found to be significantly weakened by the proposed method, as compared with the original Hoge's method. In the real data case, experiments using different bands of ZY-3 multispectral sensor-corrected images demonstrate the promising performance and feasibility of the proposed method, which is able to identify seams of the image stitching between sub-charge-coupled device units.