An Accurate Feature Point Matching Algorithm for Automatic Remote Sensing Image Registration

Wu, Guan-Long; Chang, Herng-Hua

doi:10.1109/dicta.2017.8227403

Cited by 5 publications

(4 citation statements)

References 19 publications

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“…Because of the variety of remote sensing image types and the lack of consistent transformation, remote sensing image registration is still a difficult task. For this task, Wu and Chang 5 proposed a robust and accurate feature point matching framework. First, an improved SIFT method is introduced for feature detection and matching.…”

Section: Related Researchmentioning

confidence: 99%

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Research on image stitching method based on improved ORB and stitching line calculation

Yang

Han

2022

J. Electron. Imag.

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Image matching is one of the hot issues in recent years. Image matching determines the effect of image mosaic to a large extent. Therefore, a stable, accurate, and fast image matching algorithm is very important for the subsequent processing of image information. Most of the traditional image matching methods have the problems of high-error matching rate and difficulty in to eliminating false matching. To resolve the shortcomings listed here, an improved algorithm was proposed in which a combined measure of Hamming distance. Similarity measurement with Tanimoto similarity measurement was adopted to dispose binary feature vectors. In addition, as an important step in the process of image mosaics, the calculation of the best mosaics can eliminate the ghosting and ghosting that may appear in the mosaics. However, the traditional dynamic planning method is easy to pass through the obstacles and form obvious stitching traces when searching for stitching lines in images with large obstacles. In view of the aforementioned content, we propose a fast and robust method to search the image difference matrix and structure difference matrix of the overlapping area of the input image according to the search strategy to determine the pixel coordinates of the splicing line. Finally, from an optimal splicing line, the experimental results show that our algorithm performs well for eliminating error matching points. Compared with traditional algorithms, matching accuracy is improved by 25%. In terms of calculating the optimal splicing line, the method proposed is faster than the traditional method and has good robustness.

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Section: Related Researchmentioning

confidence: 99%

“…Ji, Yang, and Han: Research on image stitching method based on improved ORB and stitching line calculation Journal of Electronic Imaging 051404-6 Sep∕Oct 2022 • Vol. 31(5) …”

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confidence: 99%

Research on image stitching method based on improved ORB and stitching line calculation

Yang

Han

2022

J. Electron. Imag.

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“…Precision = residual correct matches residual matches (16) Recall = residual correct matches initial correct matches (17) In Eq. (11), N points (x,y) are taken in the reference image, then points (x 1 , y 1 ) are obtain through the affine transformation model using the image registration parameter calculated by the algorithm. (x 2 , y 2 ) are obtained through the real affine transformation model as calculated by manual selection.…”

Section: B Evaluation Criterionmentioning

confidence: 99%

“…The associate editor coordinating the review of this manuscript and approving it for publication was Lefei Zhang . construct a new image in the coordinate system of the reference image [11]. Feature point matching is a challenging step in feature-based registration techniques, required to establish reliable correspondences between feature points extracted from the reference image and the sensed image.…”

Section: Introductionmentioning

confidence: 99%

A High Precision Feature Matching Method Based on Geometrical Outlier Removal for Remote Sensing Image Registration

Yang

et al. 2019

IEEE Access

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The reliability of feature matching is required for accurate remote sensing image registration. Outliers reduce the accuracy and effectiveness of feature matching. This paper proposes a novel feature matching method that utilizes the global geometric relationship of feature points in two images to eliminate outliers and preserve inliers. A mathematical model is formulated based on the similarity of the geometric relationship of feature points in the reference image and the sensed image. We also find the optimization solution through analysis and simplification of the mathematical model. The corresponding feature matching algorithm based on outlier removal is proposed according to the optimization solution. The experimental results of several remote sensing images demonstrate that our method can preserve more inliers, remove more outliers and obtain a better registration performance with higher accuracy and robustness than the state-of-the-art methods, such as SIFT, SIFT-RANSAC, SIFT-GTM, SIFT-LPM. INDEX TERMS Remote sensing, image registration, feature matching, outlier removal, mathematical model.

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An accurate and robust registration framework based on outlier removal and feature point adjustment for remote sensing images

Yang

Zhao

et al. 2021

International Journal of Remote Sensing

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An Accurate Feature Point Matching Algorithm for Automatic Remote Sensing Image Registration

Cited by 5 publications

References 19 publications

Research on image stitching method based on improved ORB and stitching line calculation

Research on image stitching method based on improved ORB and stitching line calculation

A High Precision Feature Matching Method Based on Geometrical Outlier Removal for Remote Sensing Image Registration

An accurate and robust registration framework based on outlier removal and feature point adjustment for remote sensing images

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