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

Dong, Yunyun; Jiao, Weili; Long, Tao; He, Guojin; Gong, Chengjuan

doi:10.3390/rs10111719

Cited by 20 publications

(10 citation statements)

References 48 publications

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“…For angle and scale estimation, a log-polar Fourier transform is first necessary to operate on the reference and sensed image, respectively. Then, the scale and angle can be converted to the displacements between the amplitude spectrum of log-polar Fourier transform of reference and sensed images [36].…”

Section: Methodsmentioning

confidence: 99%

Eliminating the Effect of Image Border with Image Periodic Decomposition for Phase Correlation Based Remote Sensing Image Registration

Dong

Jiao

Long

et al. 2019

Sensors

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In the remote sensing community, accurate image registration is the prerequisite of the subsequent application of remote sensing images. Phase correlation based image registration has drawn extensive attention due to its high accuracy and high efficiency. However, when the Discrete Fourier Transform (DFT) of an image is computed, the image is implicitly assumed to be periodic. In practical application, it is impossible to meet the periodic condition that opposite borders of an image are alike, and image always shows strong discontinuities across the frame border. The discontinuities cause a severe artifact in the Fourier Transform, namely the known cross structure composed of high energy coefficients along the axes. Here, this phenomenon was referred to as effect of image border. Even worse, the effect of image border corrupted its registration accuracy and success rate. Currently, the main solution is blurring out the border of the image by weighting window function on the reference and sensed image. However, the approach also inevitably filters out non-border information of an image. The existing understanding is that the design of window function should filter as little information as possible, which can improve the registration success rate and accuracy of methods based on phase correlation. In this paper, another approach of eliminating the effect of image border is proposed, namely decomposing the image into two images: one being the periodic image and the other the smooth image. Replacing the original image by the periodic one does not suffer from the effect on the image border when applying Fourier Transform. The smooth image is analogous to an error image, which has little information except at the border. Extensive experiments were carried out and showed that the novel algorithm of eliminating the image border can improve the success rate and accuracy of phase correlation based image registration in some certain cases. Additionally, we obtained a new understanding of the role of window function in eliminating the effect of image border, which is helpful for researchers to select the optimal method of eliminating the effect of image border to improve the registration success rate and accuracy.

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Section: Methodsmentioning

confidence: 99%

Eliminating the Effect of Image Border with Image Periodic Decomposition for Phase Correlation Based Remote Sensing Image Registration

Dong

Jiao

Long

et al. 2019

Sensors

Self Cite

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“…These merits make it quite feasible for multisensor image registration. When used in coarse registration, PC can be extended to deal with rotation and scale estimation without the need for initialization and iteration using the Fourier-Mellin transform [21][22][23]. For fine registration, PC can be adopted in local template matching even pointwise dense matching with subpixel estimation.…”

Section: Introductionmentioning

confidence: 99%

Robust Fine Registration of Multisensor Remote Sensing Images Based on Enhanced Subpixel Phase Correlation

Kang

Song

et al. 2020

Sensors

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Automatic fine registration of multisensor images plays an essential role in many remote sensing applications. However, it is always a challenging task due to significant radiometric and textural differences. In this paper, an enhanced subpixel phase correlation method is proposed, which embeds phase congruency-based structural representation, L1-norm-based rank-one matrix approximation with adaptive masking, and stable robust model fitting into the conventional calculation framework in the frequency domain. The aim is to improve the accuracy and robustness of subpixel translation estimation in practical cases. In addition, template matching using the enhanced subpixel phase correlation is integrated to realize reliable fine registration, which is able to extract a sufficient number of well-distributed and high-accuracy tie points and reduce the local misalignment for coarsely coregistered multisensor remote sensing images. Experiments undertaken with images from different satellites and sensors were carried out in two parts: tie point matching and fine registration. The results of qualitative analysis and quantitative comparison with the state-of-the-art area-based and feature-based matching methods demonstrate the effectiveness and reliability of the proposed method for multisensor matching and registration.

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“…Many local features are developed from the SIFT, such as the Affine-SIFT [6], the UC (Uniform Competency-based) features [7], and the PSO-SIFT [8]. Some studies establish feature descriptors by structure attributes instead of intensities, examples include the HOPC [9], the RIFT [10], the MSPC [11], and the MPFT [12]. Some works attempt to increase the percentage of correct correspondences through the Markov random field [13] and the Gaussian field [14] The combination of the feature-based and the area-based methods has been studied [15,16].…”

Section: Introductionmentioning

confidence: 99%

Matching RGB and Infrared Remote Sensing Images with Densely-Connected Convolutional Neural Networks

Zhu

et al. 2019

Remote Sensing

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We develop a deep learning-based matching method between an RGB (red, green and blue) and an infrared image that were captured from satellite sensors. The method includes a convolutional neural network (CNN) that compares the RGB and infrared image pair and a template searching strategy that searches the correspondent point within a search window in the target image to a given point in the reference image. A densely-connected CNN is developed to extract common features from different spectral bands. The network consists of a series of densely-connected convolutions to make full use of low-level features and an augmented cross entropy loss to avoid model overfitting. The network takes band-wise concatenated RGB and infrared images as the input and outputs a similarity score of the RGB and infrared image pair. For a given reference point, the similarity scores within the search window are calculated pixel-by-pixel, and the pixel with the highest score becomes the matching candidate. Experiments on a satellite RGB and infrared image dataset demonstrated that our method obtained more than 75% improvement on matching rate (the ratio of the successfully matched points to all the reference points) over conventional methods such as SURF, RIFT, and PSO-SIFT, and more than 10% improvement compared to other most recent CNN-based structures. Our experiments also demonstrated high performance and generalization ability of our method applying to multitemporal remote sensing images and close-range images.

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

Cited by 20 publications

References 48 publications

Eliminating the Effect of Image Border with Image Periodic Decomposition for Phase Correlation Based Remote Sensing Image Registration

Eliminating the Effect of Image Border with Image Periodic Decomposition for Phase Correlation Based Remote Sensing Image Registration

Robust Fine Registration of Multisensor Remote Sensing Images Based on Enhanced Subpixel Phase Correlation

Matching RGB and Infrared Remote Sensing Images with Densely-Connected Convolutional Neural Networks

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