Image Registration Using Log Polar Transform and Phase Correlation to Recover Higher Scale

Image registration is a very fundamental and important part in many multi-sensor image based applications. Phase correlation-based image registration method is widely concerned for its small computation amount, strong anti-interference property. However, it can only solve the image registration problem with translational motion. Hence, we proposed a modified phase correlation registration method in the paper. We analyzed the principle of registration, gave the flow chart, and applied the method to the SAR image registration problems with scaling, rotation and translation transformation. Simulation results show that the method can accurately estimate the translation parameters, zoom scale and rotation angle of registrating image relative to the reference image.

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A Novel Coarse-to-Fine Scheme for Remote Sensing Image Registration Based on SIFT and Phase Correlation

Remote Sensing ◽

10.3390/rs11151833 ◽

2019 ◽

Vol 11 (15) ◽

pp. 1833 ◽

Cited By ~ 5

Author(s):

Han Yang ◽

Xiaorun Li ◽

Liaoying Zhao ◽

Shuhan Chen

Keyword(s):

Remote Sensing ◽

Image Registration ◽

Phase Correlation ◽

Mean Square ◽

Extended Phase ◽

Registration Method ◽

Coarse Registration ◽

Feature Based ◽

Coarse To Fine ◽

Fine Registration

Automatic image registration has been wildly used in remote sensing applications. However, the feature-based registration method is sometimes inaccurate and unstable for images with large scale difference, grayscale and texture differences. In this manuscript, a coarse-to-fine registration scheme is proposed, which combines the advantage of feature-based registration and phase correlation-based registration. The scheme consists of four steps. First, feature-based registration method is adopted for coarse registration. A geometrical outlier removal method is applied to improve the accuracy of coarse registration, which uses geometric similarities of inliers. Then, the sensed image is modified through the coarse registration result under affine deformation model. After that, the modified sensed image is registered to the reference image by extended phase correlation. Lastly, the final registration results are calculated by the fusion of the coarse registration and the fine registration. High universality of feature-based registration and high accuracy of extended phase correlation-based registration are both preserved in the proposed method. Experimental results of several different remote sensing images, which come from several published image registration papers, demonstrate the high robustness and accuracy of the proposed method. The evaluation contains root mean square error (RMSE), Laplace mean square error (LMSE) and red–green image registration results.

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An Extension of Phase Correlation-Based Image Registration to Estimate Similarity Transform Using Multiple Polar Fourier Transform

Remote Sensing ◽

10.3390/rs10111719 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1719 ◽

Cited By ~ 8

Author(s):

Yunyun Dong ◽

Weili Jiao ◽

Tengfei Long ◽

Guojin He ◽

Chengjuan Gong

Keyword(s):

Fourier Transform ◽

Image Registration ◽

Fractional Fourier Transform ◽

Correlation Method ◽

High Accuracy ◽

Phase Correlation ◽

Estimation Methods ◽

Registration Method ◽

Similarity Transform ◽

Polar Grid

Image registration is a core technology of many different image processing areas and is widely used in the remote sensing community. The accuracy of image registration largely determines the effect of subsequent applications. In recent years, phase correlation-based image registration has drawn much attention because of its high accuracy and efficiency as well as its robustness to gray difference and even slight changes in content. Many researchers have reported that the phase correlation method can acquire a sub-pixel accuracy of 1 / 10 or even 1 / 100 . However, its performance is acquired only in the case of translation, which limits the scope of the application of the method. However, there are few reports on the estimation of scales and angles based on the phase correlation method. To take advantage of the high accuracy property and other merits of phase correlation-based image registration and extend it to estimate the similarity transform, we proposed a novel algorithm, the Multilayer Polar Fourier Transform (MPFT), which uses a fast and accurate polar Fourier transform with different scaling factors to calculate the log-polar Fourier transform. The structure of the polar grids of MPFT is more similar to the one of the log-polar grid. In particular, for rotation estimation only, the polar grid of MPFT is the calculation grid. To validate its effectiveness and high accuracy in estimating angles and scales, both qualitative and quantitative experiments were carried out. The quantitative experiments included a numerical simulation as well as synthetic and real data experiments. The experimental results showed that the proposed method, MPFT, performs better than the existing phase correlation-based similarity transform estimation methods, the Pseudo-polar Fourier Transform (PPFT) and the Multilayer Fractional Fourier Transform method (MLFFT), and the classical feature-based registration method, Scale-Invariant Feature Transform (SIFT), and its variant, ms-SIFT.

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Image Registration Using Blur-Invariant Phase Correlation

IEEE Signal Processing Letters ◽

10.1109/lsp.2006.891338 ◽

2007 ◽

Vol 14 (7) ◽

pp. 449-452 ◽

Cited By ~ 36

Author(s):

Ville Ojansivu ◽

Janne Heikkila

Keyword(s):

Image Registration ◽

Phase Correlation

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Robust 1-D equivalent of phase-correlation image registration algorithm

Electronics Letters ◽

10.1049/el:19860263 ◽

1986 ◽

Vol 22 (7) ◽

pp. 386 ◽

Cited By ~ 3

Author(s):

C. Morandi ◽

F. Piazza ◽

R. Capancioni

Keyword(s):

Image Registration ◽

Phase Correlation ◽

Registration Algorithm ◽

Image Registration Algorithm ◽

Correlation Image

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Seamless Mosaicking of UAV-Based Push-Broom Hyperspectral Images for Environment Monitoring

Remote Sensing ◽

10.3390/rs13224720 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4720

Author(s):

Lina Yi ◽

Jing M. Chen ◽

Guifeng Zhang ◽

Xiao Xu ◽

Xing Ming ◽

...

Keyword(s):

Image Registration ◽

Image Fusion ◽

Hyperspectral Image ◽

Weighted Average ◽

Phase Correlation ◽

Hyperspectral Images ◽

Alternative Methods ◽

Fusion Method ◽

Environment Monitoring ◽

Geometrical Accuracy

This paper proposes a systematic image mosaicking methodology to produce hyperspectral image for environment monitoring using an emerging UAV-based push-broom hyperspectral imager. The suitability of alternative methods in each step is assessed by experiments of an urban scape, a river course and a forest study area. First, the hyperspectral image strips were acquired by sequentially stitching the UAV images acquired by push-broom scanning along each flight line. Next, direct geo-referencing was applied to each image strip to get initial geo-rectified result. Then, with ground control points, the curved surface spline function was used to transform the initial geo-rectified image strips to improve their geometrical accuracy. To further remove the displacement between pairs of image strips, an improved phase correlation (IPC) and a SIFT and RANSAC-based method (SR) were used in image registration. Finally, the weighted average and the best stitching image fusion method were used to remove the spectral differences between image strips and get the seamless mosaic. Experiment results showed that as the GCPs‘ number increases, the mosaicked image‘s geometrical accuracy increases. In image registration, there exists obvious edge information that can be accurately extracted from the urban scape and river course area; comparative results can be achieved by the IPC method with less time cost. However, for the ground objects with complex texture like forest, the edges extracted from the image is prone to be inaccurate and result in the failure of the IPC method, and only the SR method can get a good result. In image fusion, the best stitching fusion method can get seamless results for all three study areas. Whereas, the weighted average fusion method was only useful in eliminating the stitching line for the river course and forest areas but failed for the urban scape area due to the spectral heterogeneity of different ground objects. For different environment monitoring applications, the proposed methodology provides a practical solution to seamlessly mosaic UAV-based push-broom hyperspectral images with high geometrical accuracy and spectral fidelity.

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