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Remote Sensing Image Registration Based on Improved KAZE and BRIEF Descriptor

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Abstract

Remote sensing image registration is still a challenging task owing to the significant influence of nonlinear differences between remote sensing images. To solve this problem, this paper proposes a novel approach with regard to feature-based remote sensing image registration. There are two key contributions: 1) we bring forward an improved strategy of composite nonlinear diffusion filtering according to the scale factors in multi-scale space and 2) we design a gradually decreasing resolution of multi-scale pyramid space. And a binary code string is served as feature descriptors to improve matching efficiency. Extensive experiments of different categories of remote image datasets on feature extraction and feature registration are performed. The experimental results demonstrate the superiority of our proposed scheme compared with other classical algorithms in terms of correct matching ratio, accuracy and computation efficiency.

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References

  1. B. Zitová, J. Flusser. Image registration methods: A survey. Image and Vision Computing, vol. 21, no. 11, pp. 977–1000, 2003. DOI: https://doi.org/10.1016/S0262-8856(03)00137-9.

    Article  Google Scholar 

  2. C. H. Tsai, Y. C. Lin. An accelerated image matching technique for UAV orthoimage registration. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 128, pp. 130–145. 2017. DOI: https://doi.org/10.1016/j.isprsjprs.2017.03.017.

    Article  Google Scholar 

  3. M. Belgiu, A. Stein. Spatiotemporal image fusion in remote sensing. Remote sensing, vol. 117, Article number 818, 2019. DOI: https://doi.org/10.3390/rs11070818.

  4. H. Liu, G. F. Xiao, Y. L. Tan, C. J. Ouyang. Multi-source remote sensing image registration based on Contourlet transform and multiple feature fusion. International Journal of Automation and Computing, vol. 16, no. 5, pp. 575–588, 2019. DOI: https://doi.org/10.1007/s11633-018-1163-6.

    Article  Google Scholar 

  5. S. Mishra, P. Shrivastava, P. Dhurvey. Change detection techniques in remote sensing — a review. International Journal of Wireless and Mobile for Industrial Systems, vol. 4, no. 1, pp. 1–8, 2017. DOI: https://doi.org/10.21742/ijwmcis.2017.4.1.01.

    Article  Google Scholar 

  6. J. M. Murphy, J. Le Moigne, D. J. Harding. Automatic image registration of multimodal remotely sensed data with global shearlet features. IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 3, pp. 1685–1704, 2016. DOI: https://doi.org/10.1109/TGRS.2015.2487457.

    Article  Google Scholar 

  7. S. Paul, U. C. Pati. Remote sensing optical image registration using modified uniform robust SIFT. IEEE Geoscience and Remote Sensing Letters, vol. 13, no. 9, pp. 1300–1304, 2016. DOI: https://doi.org/10.1109/LGRS.2016.2582528.

    Article  Google Scholar 

  8. J. Y. Ma, Y. Ma, C. Li. Infrared and visible image fusion methods and applications: A survey. Information Fusion, vol. 45, pp. 153–178. 2019. DOI: https://doi.org/10.1016/j.inffus.2018.02.004.

    Article  Google Scholar 

  9. C. Harris, M. Stephens. A combined corner and edge detector. In Proceedings of the 4th Alvey Vision Conference, Manchester, UK, pp. 147–151, 1988. DOI: https://doi.org/10.5244/C.2.23.

    Google Scholar 

  10. S. M. Smith, Brady J. M. SUSAN-A new approach to low level image processing. International Journal of Computer Vision, vol. 23, no. 1, pp. 45–78, 1997. DOI: https://doi.org/10.1023/A:1007963824710.

    Article  Google Scholar 

  11. D. G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, 2004. DOI: https://doi.org/10.1023/B:VISI.0000029664.99615.94.

    Article  Google Scholar 

  12. H. Bay, A. Ess, T. Tuytelaars, L. Van Gool. Speeded-up robust features (SURF). Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346–359, 2008. DOI: https://doi.org/10.1016/j.cviu.2007.09.014.

    Article  Google Scholar 

  13. E. Rublee, V. Rabaud, K. Konolige, G. Bradski. ORB: An efficient alternative to SIFT or SURF. In Proceedings of IEEE International Conference on Computer Vision, IEEE, Barcelona, Spain, pp. 2564–2571, 2011. DOI: https://doi.org/10.1109/ICCV.2011.6126544.

    Google Scholar 

  14. S. Leutenegger, M. Chli, R. Y. Siegwart. BRISK: Binary robust invariant scalable keypoints. In Proceedings of IEEE International Conference on Computer Vision, IEEE, Barcelona, Spain, pp. 2548–2555, 2011. DOI: https://doi.org/10.1109/ICCV.2011.6126542.

    Google Scholar 

  15. A. Sedaghat, H. Ebadi. Remote sensing image matching based on adaptive binning SIFT descriptor. IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 10, pp. 5283–5293, 2015. DOI: https://doi.org/10.1109/TGRS.2015.2420659.

    Article  Google Scholar 

  16. J. Y. Ma, J. J. Jiang, H. B. Zhou, J. Zhao, X. J. Guo. Guided locality preserving feature matching for remote sensing image registration. IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 8, pp. 4435–4447, 2018. DOI: https://doi.org/10.1109/TGRS.2018.2820040.

    Article  Google Scholar 

  17. S. H. Chen, X. R. Li, L. Y. Zhao, H. Yang. Medium-low resolution multisource remote sensing image registration based on SIFT and robust regional mutual information. International Journal of Remote Sensing, vol. 39, no. 10, pp. 3215–3242, 2018. DOI: https://doi.org/10.1080/01431161.2018.1437295.

    Article  Google Scholar 

  18. B. Li, H. Ye. RSCJ: Robust sample consensus judging algorithm for remote sensing image registration. IEEE Geoscience and Remote Sensing Letters, vol. 9, no. 4, pp. 574–578, 2012. DOI: https://doi.org/10.1109/LGRS.2011.2175434.

    Article  MathSciNet  Google Scholar 

  19. J. Y. Ma, H. B. Zhou, J. Zhou, Y. Gao, J. J. Jiang, J. W. Tian. Robust feature matching for remote sensing image registration via locally linear transforming. IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 12, pp. 6469–6481, 2015. DOI: https://doi.org/10.1109/TGRS.2015.2441954.

    Article  Google Scholar 

  20. J. Y. Ma, J. Zhou, J. J. Jiang, H. B. Zhou, X. J. Guo. Locality preserving matching. International Journal of Computer Vision, vol. 127, no. 5, pp. 512–531, 2019. DOI: https://doi.org/10.1007/s11263-018-1117-z.

    Article  MathSciNet  Google Scholar 

  21. X. Y. Jiang, J. J. Jiang, A. X. Fan, Z. Y. Wang, J. Y. Ma. Multiscale locality and rank preservation for robust feature matching of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 9, pp. 6462–6472, 2019. DOI: https://doi.org/10.1109/TGRS.2019.2906183.

    Article  Google Scholar 

  22. G. R. Cai, P. M. Jodoin, S. Z. Li, Y. D. Wu, S. Z. Su, Z. K. Huang. Perspective-SIFT: An efficient tool for low-altitude remote sensing image registration. Signal Processing, vol. 93, no. 11, pp. 3088–3110, 2013. DOI: https://doi.org/10.1016/j.sigpro.2013.04.008.

    Article  Google Scholar 

  23. B. Fan, C. L. Huo, C. H. Pan, Q. Q. Kong. Registration of optical and SAR satellite images by exploring the spatial relationship of the improved SIFT. IEEE Geoscience and Remote Sensing Letters, vol. 10, no. 4, pp. 657–661, 2013. DOI: https://doi.org/10.1109/LGRS.2012.2216500.

    Article  Google Scholar 

  24. Y. Han, J. Choi, Y. Byun, Y. Kim. Parameter optimization for the extraction of matching points between high-resolution multisensor images in urban areas. IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 9, pp. 5612–5621, 2014. DOI: https://doi.org/10.1109/TGRS.2013.2291001.

    Article  Google Scholar 

  25. B. Kupfer, N. S. Netanyahu, I. Shimshoni. An efficient SIFT-based mode-seeking algorithm for sub-pixel registration of remotely sensed images. IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 2, pp. 379–383, 2015. DOI: https://doi.org/10.1109/LGRS.2014.2343471.

    Article  Google Scholar 

  26. F. Dellinger, J. Delon, Y. Gousseau, J. Michel, F. Tupin. SAR-SIFT: A SIFT-like algorithm for SAR images. IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 1, pp. 453–466, 2015. DOI: https://doi.org/10.1109/TGRS.2014.2323552.

    Article  Google Scholar 

  27. Y. X. Ye, J. Shan. A local descriptor based registration method for multispectral remote sensing images with nonlinear intensity differences. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 90, pp. 83–95. 2014. DOI: https://doi.org/10.1016/j.isprsjprs.2014.01.009.

    Article  Google Scholar 

  28. P. E. Alcantarilla, A. Bartoli, A. J. Davison. KAZE features. In Proceedings of the 12th European Conference on Computer Vision, Springer, Florence, Italy, pp. 214–227, 2012. DOI: https://doi.org/10.1007/978-3-642-33783-316.

    Google Scholar 

  29. J. W. Fan, Y. Wu, F. Wang, Q. Zhang, G. S. Liao, M. Li. SAR image registration using phase congruency and nonlinear diffusion-based SIFT. IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 3, pp. 562–566, 2015. DOI: https://doi.org/10.1109/LGRS.2014.2351396.

    Article  Google Scholar 

  30. Q. L. Li, S. W. Qi, Y. Y. Shen, D. Ni, H. S. Zhang, T. F. Wang. Multispectral image alignment with nonlinear scale-invariant keypoint and enhanced local feature matrix. IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 7, pp. 1551–1555, 2015. DOI: https://doi.org/10.1109/LGRS.2015.2412955.

    Article  Google Scholar 

  31. J. Weickert, B. M. T. H. Romeny, M. A. Viergever. Efficient and reliable schemes for nonlinear diffusion filtering. IEEE Transactions on Image Processing, vol. 7, no. 3, pp. 398–410, 1988. DOI: https://doi.org/10.1109/83.661190.

    Article  Google Scholar 

  32. M. Calonder, V. Lepetit, C. Strecha, P. Fua. BRIEF: Binary robust independent elementary features. In Proceedings of European Conference on Computer Vision, Springer, Heraklion, Greece, pp. 778–792, 2010. DOI: https://doi.org/10.1007/978-3-642-15561-156.

    Google Scholar 

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Acknowledgments

This work was supported by National Nature Science Foundation of China (Nos. 61640412 and 61762052), the Natural Science Foundation of Jiangxi Province (No. 20192BAB207021), the Science and Technology Research Projects of Jiangxi Province Education Department (Nos. GJJ170633 and GJJ170632).

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Authors and Affiliations

  1. College of Electronic and Information Engineering, Jinggangshan University & Jiangxi Engineering Laboratory of Internet of Things (IoT) Technologies for Crop Growth, Ji’an, 343009, China

    Huan Liu

  2. College of Mechanical and Electronic, Jinggangshan University, Ji’an, 343009, China

    Gen-Fu Xiao

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  1. Huan Liu
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  2. Gen-Fu Xiao
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Correspondence to Gen-Fu Xiao.

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Recommended by Associate Editor Bin Luo

Huan Liu received the B. Sc. degree in computer science and technology from Nanjin Institute of Technology, China in 2004, received the M. Sc. degree in software engineering from Jiangxi Normal University, China in 2008, and the Ph. D. degree in pattern recognition and intelligent system from Donghua University, China in 2014. She is currently an associate professor at Jinggangshan University, China. Her research interests include machine vision, image processing and intelligent algorithm.

Gen-Fu Xiao received the B. Sc. and M. Sc. degrees in automation from Nanchang University, China in 1998 and 2005, respectively, and the Ph. D. degree in mechatronic engineering from Nanchang University, China in 2014. He is currently an associate professor in School of Mechanical and Electrical Engineering at Jinggangshan University, China. His research interests include modeling and optimization.

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Liu, H., Xiao, GF. Remote Sensing Image Registration Based on Improved KAZE and BRIEF Descriptor. Int. J. Autom. Comput. 17, 588–598 (2020). https://doi.org/10.1007/s11633-019-1218-3

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