An ASIFT-based local registration method for satellite imagery

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Wang, X., LI, Y., Wei, H. ORCID: https://orcid.org/0000-0002-9664-5748 and Liu, F. (2015) An ASIFT-based local registration method for satellite imagery. Remote Sensing, 7 (6). pp. 7044-7061. ISSN 2072-4292 doi: 10.3390/rs70607044

Abstract/Summary

Imagery registration is a fundamental step, which greatly affects later processes in image mosaic, multi-spectral image fusion, digital surface modelling, etc., where the final solution needs blending of pixel information from more than one images. It is highly desired to find a way to identify registration regions among input stereo image pairs with high accuracy, particularly in remote sensing applications in which ground control points (GCPs) are not always available, such as in selecting a landing zone on an outer space planet. In this paper, a framework for localization in image registration is developed. It strengthened the local registration accuracy from two aspects: less reprojection error and better feature point distribution. Affine scale-invariant feature transform (ASIFT) was used for acquiring feature points and correspondences on the input images. Then, a homography matrix was estimated as the transformation model by an improved random sample consensus (IM-RANSAC) algorithm. In order to identify a registration region with a better spatial distribution of feature points, the Euclidean distance between the feature points is applied (named the S criterion). Finally, the parameters of the homography matrix were optimized by the Levenberg–Marquardt (LM) algorithm with selective feature points from the chosen registration region. In the experiment section, the Chang’E-2 satellite remote sensing imagery was used for evaluating the performance of the proposed method. The experiment result demonstrates that the proposed method can automatically locate a specific region with high registration accuracy between input images by achieving lower root mean square error (RMSE) and better distribution of feature points.

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Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/41208
Identification Number/DOI	10.3390/rs70607044
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Uncontrolled Keywords	satellite remote sensing; local image registration; image mosaic; ASIFT; RANSAC
Publisher	MDPI
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Date Deposited:	19 Aug 2015 15:16	Date item deposited into CentAUR
Last Modified:	23 Jul 2024 14:01	Date item last modified

References

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