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3D Research

, 9:52 | Cite as

Feature Matching Improvement through Merging Features for Remote Sensing Imagery

  • Shahid Karim
  • Ye Zhang
  • Ali Anwar Brohi
  • Muhammad Rizwan Asif
3DR Express
  • 34 Downloads
Part of the following topical collections:
  1. Object detection and Recognition

Abstract

Feature matching is the core stage for object recognition, tracking and several applications of computer vision. Low resolution images have various limitations with respect to spatial, spectral, pixel and temporal information which reduces the performance of image processing approaches. We have combined SURF features with FAST and BRISK features individually in order to provide an optimal solution for feature matching. Furthermore, feature matching has exploited through combined features and compared the performance with state-of-the-art methods. Lastly, RANSAC and MSAC were utilized to eliminate the wrong matches to get optimal feature matches. The experimental results show that the combination of FAST–SURF and BRISK–SURF perform feature matching optimally according to the number of feature matches and estimated time.

Keywords

Feature matching SURF FAST BRISK RANSAC 

Notes

Acknowledgement

This work was supported by the National Natural Science Foundation of China under Grants 61471148.

References

  1. 1.
    Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.MathSciNetCrossRefGoogle Scholar
  2. 2.
    Trajković, M., & Hedley, M. (1998). Fast corner detection. Image and Vision Computing, 16(2), 75–87.CrossRefGoogle Scholar
  3. 3.
    Bay, H., Tuytelaars, T., & Gool, L. V. (2006). Surf: Speeded up robust features. In European conference on computer vision (pp. 404–417). Springer, Berlin.CrossRefGoogle Scholar
  4. 4.
    Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. (2011). ORB: An efficient alternative to SIFT or SURF. In 2011 IEEE international conference on computer vision (ICCV) (pp. 2564–2571). IEEE.Google Scholar
  5. 5.
    Leutenegger, S., Chli, M., & Siegwart, R. Y. (2011). BRISK: Binary robust invariant scalable keypoints. In 2011 IEEE international conference on computer vision (ICCV) (pp. 2548–2555). IEEE.Google Scholar
  6. 6.
    Karim, S., Zhang, Y., Asif, M. R., & Ali, S. (2017). Comparative analysis of feature extraction methods in satellite imagery. Journal of Applied Remote Sensing, 11(4), 042618.CrossRefGoogle Scholar
  7. 7.
    Harris, C. G., & Stephens, M. J. (1988). A combined corner and edge detector. In Alvey vision conference, Manchester, UK (pp. 147–151).Google Scholar
  8. 8.
    Azad, P., Asfour, T., & Dillmann, R. (2009). Combining Harris interest points and the SIFT descriptor for fast scale-invariant object recognition. In IEEE/RSJ international conference on intelligent robots and systems, 2009. IROS 2009 (pp. 4275–4280). IEEE.Google Scholar
  9. 9.
    Fischler, M. A., & Bolles, R. C. (1981). Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6), 381–395.MathSciNetCrossRefGoogle Scholar
  10. 10.
    Torr, P. H. S., & Zisserman, A. (2000). MLESAC: A new robust estimator with application to estimating image geometry. Computer Vision and Image Understanding, 78(1), 138–156.CrossRefGoogle Scholar
  11. 11.
    Karami, E., Prasad, S., & Shehata, M. (2017). Image matching using SIFT, SURF, BRIEF and ORB: Performance comparison for distorted images. arXiv preprint arXiv:1710.02726.
  12. 12.
    Ren, S., & Li, J. (2018). An improved matching method base on SURF. In Tenth international conference on digital image processing (ICDIP 2018) (Vol. 10806, p. 108062D). International Society for Optics and Photonics.Google Scholar
  13. 13.
    Guerrero, M. (2011). A comparative study of three image matching algorithms: Sift, surf, and fast. All Graduate Theses and Dissertations. Paper 1040. https://digitalcommons.usu.edu/etd/1040. Accessed 12 Sept 2018.
  14. 14.
    Jiang, Z., Wang, Q., & Cui, Y. (2011). A fast method for feature matching based on SURF. In International conference on intelligent science and intelligent data engineering (pp. 374–381). Springer, Berlin.CrossRefGoogle Scholar
  15. 15.
    Huijuan, Z., & Qiong, H. (2011). Fast image matching based-on improved SURF algorithm. In 2011 International conference on electronics, communications and control (ICECC) (pp. 1460–1463). IEEE.Google Scholar
  16. 16.
    Hutama, S. A., Nugroho, S., & Utomo, D. (2016). Features deletion on multiple objects recognition using speeded-up robust features, scale invariant feature transform and randomized KD-tree. TELKOMNIKA (Telecommunication Computing Electronics and Control), 14(2), 692–698.CrossRefGoogle Scholar
  17. 17.
    Pan, J., Chen, W., & Peng, W. (2013). A new moving objects detection method based on improved SURF algorithm. In Control and decision conference (CCDC), 2013 25th Chinese (pp. 901–906). IEEE.Google Scholar
  18. 18.
    Tong, L., & Ying, X. (2018). 3D Point cloud initial registration using surface curvature and SURF matching. 3D Research, 9(3), 41.CrossRefGoogle Scholar
  19. 19.
    Zitova, B., & Flusser, J. (2003). Image registration methods: a survey. Image and Vision Computing, 21(11), 977–1000.CrossRefGoogle Scholar
  20. 20.
    Razakarivony, S., & Jurie, F. (2016). Vehicle detection in aerial imagery: A small target detection benchmark. Journal of Visual Communication and Image Representation, 34, 187–203.CrossRefGoogle Scholar
  21. 21.
    Kim, T., & Im, Y.-J. (2003). Automatic satellite image registration by combination of matching and random sample consensus. IEEE Transactions on Geoscience and Remote Sensing, 41(5), 1111–1117.CrossRefGoogle Scholar
  22. 22.
    Gu, L. (2007). An automatic target image mosaic algorithm. In The international conference on EUROCON, 2007. Computer as a tool (pp. 369–374). IEEE.Google Scholar
  23. 23.
    Wei, W., Jun, H., & Yiping, T. (2008). Image matching for geomorphic measurement based on SIFT and RANSAC methods. In 2008 international conference on computer science and software engineering (pp. 317–320). IEEE.Google Scholar

Copyright information

© 3D Research Center, Kwangwoon University and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Shahid Karim
    • 1
  • Ye Zhang
    • 1
  • Ali Anwar Brohi
    • 2
  • Muhammad Rizwan Asif
    • 3
  1. 1.School of Electronics and Information EngineeringHarbin Institute of TechnologyHarbinChina
  2. 2.School of Energy Science and EngineeringHarbin Institute of TechnologyHarbinChina
  3. 3.School of Electronics and Information EngineeringXi’an Jiaotong UniversityXi’anChina

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