Optimal Euclidean distance matrix matching method for synthetic aperture radar images

被引：1

作者：

Zeng L. ^{[1
]}

Zhou D. ^{[1
]}

Pan Q. ^{[1
]}

Zhang K. ^{[1
]}

机构：

[1] School of Electronics and Information, Northwestern Polytechnical University, Xi'an

来源：

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2017年 / 39卷 / 05期

关键词：

Feature vector; Image registration; Scale invariant feature transform (SIFT); Synthetic aperture radar (SAR);

D O I：

10.3969/j.issn.1001-506X.2017.05.08

中图分类号：

学科分类号：

摘要：

In the application of synthetic aperture radar (SAR) image registration based on the scale invariant feature transform (SIFT) algorithm, a keypoint is always assigned with several dominant orientations. Thought the number of matches is increased, the feature matching performance usually decreases significantly with the infuluence of the feature vectors extracted with different orientations. An optimal Euclidean distance matrix (OEDM) is proposed for two sets of feature vectors to enhance the matching performance. The most similar keypoints are selected from the OEDM. In addition, spatial consistency of the keypoints from the two images is maintained by calculating the transformed distances, and the incorrect matches are eliminated effectively. Comparison with traditional dual matching (DM) methods is performed. The experimental results demonstrate the superiorities of the proposed method in both accuracy and efficiency. © 2017, Editorial Office of Systems Engineering and Electronics. All right reserved.

引用

页码：1002 / 1006

页数：4

共 16 条

[1]

Shuo L., Gao Z.J., SAR image target detection in complex environments based on improved visual attention algorithm, EURASIP Journal on Wireless Communications and Networking, 2014, 1, pp. 2-8, (2014)

[2]

Cossu R., Dellacqua F., Polli D.A., Et al., SAR-based seismic damage assessment in urban areas: scaling down resolution, scaling up computational performance, IEEE Geoscience & Remote Sensing Letters, 5, 4, pp. 1110-1117, (2012)

[3]

Fan J., Wu Y., Wang F., Et al., SAR image registration using phase congruency and nonlinear diffusion-based SIFT, IEEE Geoscience & Remote Sensing Letters, 12, 3, pp. 562-566, (2015)

[4]

Lee M.H., Park I.K., Robust feature description and matching using local graph, Proc. of the Signal and Information Processing Association Annual Summit and Conference, pp. 1-4, (2013)

[5]

Zhang H., Ni W.P., Yan W.D., Et al., Robust SAR image registration based on edge matching and refined coherent point drift, IEEE Geoscience & Remote Sensing Letters, 12, 10, pp. 2115-2119, (2015)

[6]

Lowe D.G., Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision, 60, 2, pp. 91-110, (2004)

[7]

Wang F., You H., Fu X., Adapted anisotropic Gaussian SIFT matching strategy for SAR registration, IEEE Geoscience & Remote Sensing Letters, 12, 1, pp. 160-164, (2015)

[8]

Dellinger F., Delon J., Gousseau Y., Et al., SAR-SIFT: a SIFT-like algorithm for SAR images, IEEE Trans. on Geoscience Remote Sensings, 53, 1, pp. 453-466, (2015)

[9]

Wang B., Zhang J., Lu L., Et al., A uniform SIFT-like algorithm for SAR image registration, IEEE Geoscience & Remote Sensing Letters, 12, 7, pp. 1426-1430, (2015)

[10]

Bastanlar Y., Temizel A., Yardmc Y., Improved SIFT matching for image pairs with scale difference, Electronics Letters, 46, 3, pp. 346-348, (2010)

← 1 2 →