More Web Proxy on the site http://driver.im/

research-article

Image registration of simulated synthetic aperture sonar images using SIFT

Authors:

Michael HayesAuthors Info & Claims

IVCNZ '14: Proceedings of the 29th International Conference on Image and Vision Computing New Zealand

Pages 31 - 36

https://doi.org/10.1145/2683405.2683407

Published: 19 November 2014 Publication History

Abstract

The scale-invariant feature transform (SIFT) algorithm is investigated for registration of two synthetic aperture sonar images. The image registration geometry for two ideal sonar tracks is presented. The sonar track parameters (and thus the registration transform) can be fully determined from two exact non-degenerate correspondences and the altitude of one of the tracks. SIFT yielded 99% inlier correspondences and demonstrated sub-pixel accuracy. RANSAC was used to select inliers within a squared pixel error tolerance. The proposed feature-based algorithm yielded estimations of the tracks corresponding to a misregistration up to 0.17 pixels throughout the scene.

References

[1]

H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool. Speeded-up robust features (SURF). Computer vision and image understanding, 110(3):346--359, 2008.

Digital Library

[2]

Y. Bentoutou, N. Taleb, K. Kpalma, and J. Ronsin. An automatic image registration for applications in remote sensing. IEEE Transactions on Geoscience and Remote Sensing, 43(9):2127--2137, 2005.

[3]

F. Dellinger, J. Delon, Y. Gousseau, J. Michel, and F. Tupin. SAR-SIFT: A SIFT-like algorithm for applications on SAR images. In International Geoscience and Remote Sensing Symposium (IGARSS), pages 3478--3481. IEEE, 2012.

[4]

B. Fan, F. Wu, and Z. Hu. Aggregating gradient distributions into intensity orders: A novel local image descriptor. In Computer Vision and Pattern Recognition (CVPR), pages 2377--2384. IEEE, 2011.

Digital Library

[5]

M. A. Fischler and R. C. Bolles. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6):381--395, 1981.

Digital Library

[6]

T. G-Michael, B. Marchand, J. D. Tucker, D. D. Sternlicht, T. M. Marston, and M. R. Azimi-Sadjadi. Automated change detection for synthetic aperture sonar. In SPIE Defense+ Security, pages 907204--907204. International Society for Optics and Photonics, 2014.

[7]

C. Harris and M. Stephens. A combined corner and edge detector. In Alvey vision conference, volume 15, page 50. Manchester, UK, 1988.

[8]

R. Hartley and A. Zisserman. Multiple view geometry in computer vision. Cambridge university press, 2003.

Digital Library

[9]

M. P. Hayes and P. T. Gough. Synthetic aperture sonar: a review of current status. IEEE Journal of Oceanic Engineering, 34(3):207--224, 2009.

[10]

X. Kang, C. Han, Y. Yang, and T. Tao. SAR image edge detection by ratio-based Harris method. In International Conference on Acoustics, Speech and Signal Processing, volume 2, pages 837--840. IEEE, 2006.

[11]

J.-S. Lee. Refined filtering of image noise using local statistics. Computer graphics and image processing, 15(4):380--389, 1981.

[12]

D. Li and Y. Zhang. On the appropriate feature for general SAR image registration. In Remote Sensing, pages 85360X--85360X. SPIE, 2012.

[13]

D. G. Lowe. Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2):91--110, 2004.

Digital Library

[14]

O. Midtgaard, R. E. Hansen, T. Saebo, V. Myers, J. R. Dubberley, and I. Quidu. Change detection using synthetic aperture sonar: preliminary results from the Larvik trial. In OCEANS, pages 1--8. IEEE, 2011.

[15]

L. Moisan and B. Stival. A probabilistic criterion to detect rigid point matches between two images and estimate the fundamental matrix. International Journal of Computer Vision, 57(3):201--218, 2004.

Digital Library

[16]

D. Ni, Y. Qu, X. Yang, Y. P. Chui, T.-T. Wong, S. S. Ho, and P. A. Heng. Volumetric ultrasound panorama based on 3D SIFT. In Medical Image Computing and Computer-Assisted Intervention--MICCAI, pages 52--60. Springer, 2008.

Digital Library

[17]

P. Perona and J. Malik. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7):629--639, 1990.

Digital Library

[18]

G. S. Sammelmann. Propagation and scattering in very shallow water. In OCEANS, volume 1, pages 337--344. IEEE, 2001.

[19]

P. Schwind, S. Suri, P. Reinartz, and A. Siebert. Applicability of the SIFT operator to geometric SAR image registration. International Journal of Remote Sensing, 31(8):1959--1980, 2010.

Digital Library

[20]

F. Sur. Robust matching in an uncertain world. In International Conference on Pattern Recognition (ICPR), pages 2350--2353. IEEE, 2010.

Digital Library

[21]

S. Suri, P. Schwind, J. Uhl, and P. Reinartz. Modifications in the SIFT operator for effective SAR image matching. International Journal of Image and Data Fusion, 1(3):243--256, 2010.

[22]

T. Tang, D. Xiang, H. Liu, and Y. Su. A new local feature extraction in SAR image. In Asia-Pacific Conference on Synthetic Aperture Radar (APSAR), pages 377--379. IEEE, 2013.

[23]

S. Wang, H. You, and K. Fu. BFSIFT: A novel method to find feature matches for SAR image registration. IEEE Geoscience and Remote Sensing Letters, 9(4):649--653, 2012.

[24]

Y. Yu and S. T. Acton. Speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing, 11(11):1260--1270, 2002.

Digital Library

[25]

B. Zeisl, P. F. Georgel, F. Schweiger, E. G. Steinbach, and N. Navab. Estimation of location uncertainty for scale invariant features points. In BMVC, pages 1--12, 2009.

Cited By

Shin JChang SBays MWeaver JWettergren TFerrari S(2022)Synthetic Sonar Image Simulation with Various Seabed Conditions for Automatic Target RecognitionOCEANS 2022, Hampton Roads10.1109/OCEANS47191.2022.9977275(1-8)Online publication date: 17-Oct-2022
https://doi.org/10.1109/OCEANS47191.2022.9977275
Zhang WZhou TXu CLiu M(2021)A SIFT‐Like Feature Detector and Descriptor for Multibeam Sonar ImagingJournal of Sensors10.1155/2021/88458142021:1Online publication date: 16-Jul-2021
https://doi.org/10.1155/2021/8845814
Wang VHayes M(2017)Synthetic Aperture Sonar Track Registration Using SIFT Image CorrespondencesIEEE Journal of Oceanic Engineering10.1109/JOE.2016.263407842:4(901-913)Online publication date: Oct-2017
https://doi.org/10.1109/JOE.2016.2634078
Show More Cited By

Index Terms

Image registration of simulated synthetic aperture sonar images using SIFT
1. Computing methodologies
  1. Machine learning

Recommendations

Data-driven image registration for coherent change detection of synthetic aperture sonar imagery
IVCNZ '14: Proceedings of the 29th International Conference on Image and Vision Computing New Zealand

Coherent change detection of synthetic aperture sonar images requires more accurate registration than can be provided by recorded navigation data. Instead, data-driven techniques must be used. This paper proposes measuring the position of correlation ...
Iterative Synthetic Aperture Radar Image Registration using View Synthesis (IIRVS)

Image registration is a preprocessing operation in several synthetic aperture radar (SAR) image applications. View synthesis approach is incorporated in the standard pipeline of feature based image registration, to feed feature detector with additional ...
Algorithm for Sequence Image Automatic Mosaic Based on SIFT Feature
ICIE '10: Proceedings of the 2010 WASE International Conference on Information Engineering - Volume 01

Constraining by cameras’ view-angles of the outdoor monitoring systems, the panoramic digital images fail to be obtained directly from photographing. A method is proposed on the basis of the scale invariance feature transform (i.e. SIFT) algorithm to ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

IVCNZ '14: Proceedings of the 29th International Conference on Image and Vision Computing New Zealand

November 2014

298 pages

ISBN:9781450331845

DOI:10.1145/2683405

Co-chairs:
John Perrone,
Michael Mayo,
Anthony Blake,
General Chair:
Michael J. Cree,
Publications Chair:
Lee Streeter

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

The University of Waikato

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 November 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

Office of Naval Research

Conference

IVCNZ '14

IVCNZ '14: The 29th International Conference on Image and Vision Computing New Zealand

November 19 - 21, 2014

Hamilton, New Zealand

Acceptance Rates

IVCNZ '14 Paper Acceptance Rate 55 of 74 submissions, 74%;

Overall Acceptance Rate 55 of 74 submissions, 74%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
128
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)1

Reflects downloads up to 23 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Shin JChang SBays MWeaver JWettergren TFerrari S(2022)Synthetic Sonar Image Simulation with Various Seabed Conditions for Automatic Target RecognitionOCEANS 2022, Hampton Roads10.1109/OCEANS47191.2022.9977275(1-8)Online publication date: 17-Oct-2022
https://doi.org/10.1109/OCEANS47191.2022.9977275
Zhang WZhou TXu CLiu M(2021)A SIFT‐Like Feature Detector and Descriptor for Multibeam Sonar ImagingJournal of Sensors10.1155/2021/88458142021:1Online publication date: 16-Jul-2021
https://doi.org/10.1155/2021/8845814
Wang VHayes M(2017)Synthetic Aperture Sonar Track Registration Using SIFT Image CorrespondencesIEEE Journal of Oceanic Engineering10.1109/JOE.2016.263407842:4(901-913)Online publication date: Oct-2017
https://doi.org/10.1109/JOE.2016.2634078
Wang VHayes M(2017)SIFT localisation accuracy on interpolated speckle images2017 International Conference on Image and Vision Computing New Zealand (IVCNZ)10.1109/IVCNZ.2017.8402510(1-6)Online publication date: Dec-2017
https://doi.org/10.1109/IVCNZ.2017.8402510
Wang VHayes M(2016)Analysis of feature matching performance on correlated speckle image pairsOCEANS 2016 MTS/IEEE Monterey10.1109/OCEANS.2016.7761310(1-8)Online publication date: Sep-2016
https://doi.org/10.1109/OCEANS.2016.7761310
Wang VHayes M(2016)Modelling of feature matching performance on correlated speckle images2016 International Conference on Image and Vision Computing New Zealand (IVCNZ)10.1109/IVCNZ.2016.7804419(1-6)Online publication date: Nov-2016
https://doi.org/10.1109/IVCNZ.2016.7804419

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents