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

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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A Point Pattern Chamfer Registration of Optical and SAR Images Based on Mesh Grids

Remote Sensing ◽

10.3390/rs10111837 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1837 ◽

Cited By ~ 4

Author(s):

Chu He ◽

Peizhang Fang ◽

Dehui Xiong ◽

Wenwei Wang ◽

Mingsheng Liao

Keyword(s):

Image Registration ◽

Feature Matching ◽

Point Pattern ◽

Sar Image ◽

Sar Images ◽

Coarse Registration ◽

Feature Based ◽

Mesh Grid ◽

Coarse To Fine ◽

Fine Registration

Automatic image registration of optical-to-Synthetic aperture radar (SAR) images is difficult because of the inconsistency of radiometric and geometric properties between the optical image and the SAR image. The intensity-based methods may require many calculations and be ineffective when there are geometric distortions between these two images. The feature-based methods have high requirements on features, and there are certain challenges in feature extraction and matching. A new automatic optical-to-SAR image registration framework is proposed in this paper. First, modified holistically nested edge detection is employed to detect the main contours in both the optical and SAR images. Second, a mesh grid strategy is presented to perform a coarse-to-fine registration. The coarse registration calculates the feature matching and summarizes the preliminary results for the fine registration process. Finally, moving direct linear transformation is introduced to perform a homography warp to alleviate parallax. The experimental results show the effectiveness and accuracy of our proposed method.

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An Image Registration Method Using Deep Residual Network Features for Multisource High-Resolution Remote Sensing Images

Remote Sensing ◽

10.3390/rs13173425 ◽

2021 ◽

Vol 13 (17) ◽

pp. 3425

Author(s):

Xin Zhao ◽

Hui Li ◽

Ping Wang ◽

Linhai Jing

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Image Registration ◽

Feature Descriptor ◽

Remote Sensing Images ◽

Residual Network ◽

Scale Invariant ◽

Registration Method ◽

Sensing Applications ◽

Feature Based

Accurate registration for multisource high-resolution remote sensing images is an essential step for various remote sensing applications. Due to the complexity of the feature and texture information of high-resolution remote sensing images, especially for images covering earthquake disasters, feature-based image registration methods need a more helpful feature descriptor to improve the accuracy. However, traditional image registration methods that only use local features at low levels have difficulty representing the features of the matching points. To improve the accuracy of matching features for multisource high-resolution remote sensing images, an image registration method based on a deep residual network (ResNet) and scale-invariant feature transform (SIFT) was proposed. It used the fusion of SIFT features and ResNet features on the basis of the traditional algorithm to achieve image registration. The proposed method consists of two parts: model construction and training and image registration using a combination of SIFT and ResNet34 features. First, a registration sample set constructed from high-resolution satellite remote sensing images was used to fine-tune the network to obtain the ResNet model. Then, for the image to be registered, the Shi_Tomas algorithm and the combination of SIFT and ResNet features were used for feature extraction to complete the image registration. Considering the difference in image sizes and scenes, five pairs of images were used to conduct experiments to verify the effectiveness of the method in different practical applications. The experimental results showed that the proposed method can achieve higher accuracies and more tie points than traditional feature-based methods.

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Multi-Sensor Image Registration by Combination of Relaxation Optimization Matching and Partitioning RANSAC

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.765-767.2882 ◽

2013 ◽

Vol 765-767 ◽

pp. 2882-2885

Author(s):

Ying Dan Wu ◽

Yang Ming

Keyword(s):

Image Registration ◽

Optimization Technique ◽

Registration Method ◽

Approximate Relationship ◽

Coarse Registration ◽

Relationship Of ◽

Coarse To Fine

This paper presents a multi-sensor image registration method by combination of relaxation optimization matching and Partitioning RANSAC. Firstly, the global coarse registration is performed to establish the approximate relationship of reference and slave image. Secondly, in each pyramid level, the normalized MI and relaxation optimization technique are adopted to get the matching points, and partitioning RANSAC is used to delete the existing false matches. The coarse-to-fine strategy is integrated to refine the results, and finally the rubber sheeting method is used to realize the image registration. Two datasets have been experimented, and it can be found that satisfactory registration method can be obtained.

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A Coarse-to-Fine Registration on 3D Multi-Phase Abdominal CT Images

Nanoscience and Nanotechnology Letters ◽

10.1166/nnl.2020.3194 ◽

2020 ◽

Vol 12 (7) ◽

pp. 909-914

Author(s):

Shao-Di Yang ◽

Fan Zhang ◽

Zhen Yang ◽

Xiao-Yu Yang ◽

Shu-Zhou Li

Keyword(s):

Three Dimensional ◽

Ct Images ◽

Abdominal Ct ◽

Registration Method ◽

Coarse Registration ◽

Multi Phase ◽

Coarse To Fine ◽

Aided Diagnosis ◽

Fine Registration ◽

Spatial Regularization

Registration is a technical support for the integration of nanomaterial imaging-aided diagnosis and treatment. In this paper, a coarse-to-fine three-dimensional (3D) multi-phase abdominal CT images registration method is proposed. Firstly, a linear model is used to coarsely register the paired multiphase images. Secondly, an intensity-based registration framework is proposed, which contains the data and spatial regularization terms and performs fine registration on the paired images obtained in the coarse registration step. The results illustrate that the proposed method is superior to some existing methods with the average MSE, PSNR, and SSIM values of 0.0082, 21.2695, and 0.8956, respectively. Therefore, the proposed method provides an efficient and robust framework for 3D multi-phase abdominal CT images registration.

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A novel extended phase correlation algorithm based on Log-Gabor filtering for multimodal remote sensing image registration

International Journal of Remote Sensing ◽

10.1080/01431161.2019.1579941 ◽

2019 ◽

Vol 40 (14) ◽

pp. 5429-5453 ◽

Cited By ~ 3

Author(s):

Xunwei Xie ◽

Yongjun Zhang ◽

Xiao Ling ◽

Xiang Wang

Keyword(s):

Remote Sensing ◽

Image Registration ◽

Remote Sensing Image ◽

Phase Correlation ◽

Gabor Filtering ◽

Extended Phase ◽

Correlation Algorithm ◽

Log Gabor

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Robust Coarse-to-Fine Registration Scheme for Mobile Laser Scanner Point Clouds Using Multiscale Eigenvalue Statistic-Based Descriptor

Sensors ◽

10.3390/s21072431 ◽

2021 ◽

Vol 21 (7) ◽

pp. 2431

Author(s):

Yongjian Fu ◽

Zongchun Li ◽

Wenqi Wang ◽

Hua He ◽

Feng Xiong ◽

...

Keyword(s):

Laser Scanner ◽

Point Clouds ◽

Target Point ◽

Registration Method ◽

Geometric Consistency ◽

Coarse Registration ◽

Eigenvalue Statistic ◽

Pairwise Registration ◽

Coarse To Fine ◽

Fine Registration

To overcome the drawbacks of pairwise registration for mobile laser scanner (MLS) point clouds, such as difficulty in searching the corresponding points and inaccuracy registration matrix, a robust coarse-to-fine registration method is proposed to align different frames of MLS point clouds into a common coordinate system. The method identifies the correct corresponding point pairs from the source and target point clouds, and then calculates the transform matrix. First, the performance of a multiscale eigenvalue statistic-based descriptor with different combinations of parameters is evaluated to identify the optimal combination. Second, based on the geometric distribution of points in the neighborhood of the keypoint, a weighted covariance matrix is constructed, by which the multiscale eigenvalues are calculated as the feature description language. Third, the corresponding points between the source and target point clouds are estimated in the feature space, and the incorrect ones are eliminated via a geometric consistency constraint. Finally, the estimated corresponding point pairs are used for coarse registration. The value of coarse registration is regarded as the initial value for the iterative closest point algorithm. Subsequently, the final fine registration result is obtained. The results of the registration experiments with Autonomous Systems Lab (ASL) Datasets show that the proposed method can accurately align MLS point clouds in different frames and outperform the comparative methods.

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SPPD: A Novel Reassembly Method for 3D Terracotta Warrior Fragments Based on Fracture Surface Information

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10080525 ◽

2021 ◽

Vol 10 (8) ◽

pp. 525

Author(s):

Wenmin Yao ◽

Tong Chu ◽

Wenlong Tang ◽

Jingyu Wang ◽

Xin Cao ◽

...

Keyword(s):

Fracture Surface ◽

Principal Component ◽

Point Clouds ◽

Three Dimension ◽

Registration Method ◽

Series Of Experiments ◽

Coarse To Fine ◽

Fine Registration ◽

Common Situation ◽

Better Than

As one of China′s most precious cultural relics, the excavation and protection of the Terracotta Warriors pose significant challenges to archaeologists. A fairly common situation in the excavation is that the Terracotta Warriors are mostly found in the form of fragments, and manual reassembly among numerous fragments is laborious and time-consuming. This work presents a fracture-surface-based reassembling method, which is composed of SiamesePointNet, principal component analysis (PCA), and deep closest point (DCP), and is named SPPD. Firstly, SiamesePointNet is proposed to determine whether a pair of point clouds of 3D Terracotta Warrior fragments can be reassembled. Then, a coarse-to-fine registration method based on PCA and DCP is proposed to register the two fragments into a reassembled one. The above two steps iterate until the termination condition is met. A series of experiments on real-world examples are conducted, and the results demonstrate that the proposed method performs better than the conventional reassembling methods. We hope this work can provide a valuable tool for the virtual restoration of three-dimension cultural heritage artifacts.

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