DocumentCode :
62895
Title :
A Novel Subpixel Phase Correlation Method Using Singular Value Decomposition and Unified Random Sample Consensus
Author :
Xiaohua Tong ; Zhen Ye ; Yusheng Xu ; Shijie Liu ; Lingyun Li ; Huan Xie ; Tianpeng Li
Author_Institution :
Coll. of Surveying & Geo-Inf., Tongji Univ., Shanghai, China
Volume :
53
Issue :
8
fYear :
2015
fDate :
Aug. 2015
Firstpage :
4143
Lastpage :
4156
Abstract :
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.
Keywords :
correlation methods; image denoising; image registration; randomised algorithms; remote sensing; sensor fusion; signal processing; singular value decomposition; Hoge´s method advantages; SVD-based phase correlation method; ZY-3 multispectral sensor-corrected image bands; image stitching seam identification; line fitting accuracy estimation; line fitting robustness estimation; line fitting stability estimation; pixel locking effect degree; pixel-to-pixel dense matching scheme; practical image registration; practical remote sensing data; remote sensing task; robust line fitting estimator; singular value decomposition; sub-charge-coupled device units; subpixel matching-associated pixel locking effect; subpixel phase correlation method aliasing sensitivity; subpixel phase correlation method corruption sensitivity; subpixel phase correlation method noise; subpixel translation estimation; synthetic image pair; translation estimation problem conversion; unified RANSAC algorithm; unified random sample consensus; Correlation; Estimation; Least squares approximations; Noise; Optimization; Robustness; Standards; Aliasing and noise; singular value decomposition (SVD); subpixel phase correlation; unified random sample consensus;
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
Publisher :
ieee
ISSN :
0196-2892
Type :
jour
DOI :
10.1109/TGRS.2015.2391999
Filename :
7039280
Link To Document :
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