The Study of a Fast Sub-Pixel Registration Method for Remote Sensing Image

2014 ◽  
Vol 989-994 ◽  
pp. 3877-3880 ◽  
Author(s):  
Cui Zhou ◽  
Jing Hong Zhou ◽  
Dong Hao Fan
Keyword(s):  
Remote Sensing ◽  
Fourier Transform ◽  
Remote Sensing Image ◽  
Phase Correlation ◽  
Engineering Practice ◽  
Improved Method ◽  
Registration Accuracy ◽  
Total Size ◽  
Transform Method ◽  
Registration Method

We put forward a fast and efficiently sub-pixel registration method for solving the classical methods’ problems of low efficiency, and use efficiently sub-images instead of original image to sub-pixel registration based on the Fourier transform phase correlation and matrix Fourier transform method. Effective sub-images are selected from the total size of the high-frequency energy after two-dimensional wavelet decomposition, then we use the phase correlation to calculate the pixel displacement and matrix Fourier transform to calculate the sub-pixel displacement. Not only the improved method is inherited the advantage of matrix Fourier transform sub-pixel registration, but also the registration speed is greatly improved. This is more applicable to massive remote sensing data. Through simulation and engineering practice, composited registration accuracy and speed, proved that the improved method is more efficient compared with the classical methods, and it’s more suitable for real remote sensing image registration.

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

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2329 ◽  
Author(s):  
Yunyun Dong ◽  
Weili Jiao ◽  
Tengfei Long ◽  
Lanfa Liu ◽  
Guojin He
Keyword(s):  
Remote Sensing ◽  
Fourier Transform ◽  
Image Registration ◽  
Success Rate ◽  
High Efficiency ◽  
High Energy ◽  
Phase Correlation ◽  
Window Function ◽  
Registration Accuracy ◽  
Strong Discontinuities

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.

High accuracy-automatic registration method research on multi-source remote sensing image

2016 ◽  
Vol 31 (6) ◽  
pp. 604-612
Author(s):  
程国华 CHENG Guo-hua ◽  
王阿川 WANG a-chuan ◽  
陈舒畅 CHEN Shu-chang ◽  
赵 宇 ZHAO Yu ◽  
范晓锐 FAN Xiao-rui ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
High Accuracy ◽  
Automatic Registration ◽  
Registration Method

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

2019 ◽  
Vol 11 (15) ◽  
pp. 1833 ◽  
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.

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

2018 ◽  
Vol 10 (11) ◽  
pp. 1719 ◽  
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.

An Improved Method for Remote Sensing Image Compression

2014 ◽  
Vol 543-547 ◽  
pp. 2681-2686
Author(s):  
Shuai Xue ◽  
Qing Dai
Keyword(s):  
Remote Sensing ◽  
Wavelet Transform ◽  
Image Compression ◽  
Text Analysis ◽  
Image Data ◽  
Remote Sensing Image ◽  
Integer Wavelet Transform ◽  
Improved Method ◽  
Integer Wavelet ◽  
And Storage

Consider of the characteristics of quantity of remote sensing image data, in order to transport and storage them easily, the text analysis and research some compression technology about remote sensing image, and make compares among them, then propose an optimized method based on integer wavelet transform about image compression. This method encodes the lowest frequency sub-band and high.

scholarly journals UAV Remote Sensing Image Automatic Registration Based on Deep Residual Features

2021 ◽  
Vol 13 (18) ◽  
pp. 3605
Author(s):  
Xin Luo ◽  
Guangling Lai ◽  
Xiao Wang ◽  
Yuwei Jin ◽  
Xixu He ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Rapid Development ◽  
Image Features ◽  
Automatic Registration ◽  
Remote Sensing Images ◽  
Registration Accuracy ◽  
Registration Method ◽  
Uav Images ◽  
High Level

With the rapid development of unmanned aerial vehicle (UAV) technology, UAV remote sensing images are increasing sharply. However, due to the limitation of the perspective of UAV remote sensing, the UAV images obtained from different viewpoints of a same scene need to be stitched together for further applications. Therefore, an automatic registration method of UAV remote sensing images based on deep residual features is proposed in this work. It needs no additional training and does not depend on image features, such as points, lines and shapes, or on specific image contents. This registration framework is built as follows: Aimed at the problem that most of traditional registration methods only use low-level features for registration, we adopted deep residual neural network features extracted by an excellent deep neural network, ResNet-50. Then, a tensor product was employed to construct feature description vectors through exacted high-level abstract features. At last, the progressive consistency algorithm (PROSAC) was exploited to remove false matches and fit a geometric transform model so as to enhance registration accuracy. The experimental results for different typical scene images with different resolutions acquired by different UAV image sensors indicate that the improved algorithm can achieve higher registration accuracy than a state-of-the-art deep learning registration algorithm and other popular registration algorithms.

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