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Signal, Image and Video Processing

, Volume 12, Issue 7, pp 1237–1244 | Cite as

The multiscale directional neighborhood filter and its application to clutter removal in GPR data

  • D. Kumlu
  • I. Erer
Original Paper
  • 110 Downloads

Abstract

We present a novel neighborhood filter (NF)-based clutter removal algorithm in ground-penetrating radar (GPR) images. Since NF uses only range kernel of the well-known bilateral filter, it is less complex and makes clutter removal method appropriate for real-time implementations. We extend NF to multiscale–multidirectional case: MDNF and then decompose the GPR image into approximation and detail subbands to capture the intrinsic geometrical structures that contain both target and clutter information. After directional decomposition, the clutter is eliminated by keeping the diagonal information for target component. Finally, the inverse transform is applied to the remaining subbands for reconstruction of clutter-free GPR image. Results of both simulated and real datasets validate the superiority of MDNF over the state-of-the-art methods, and it improves in the false alarm rate further by 5.5% at maximum detection performance.

Keywords

Clutter removal Image decomposition Directional filter bank Neighborhood filtering Multiscale transform Ground-penetrating radar 

References

  1. 1.
    Daniels, D.J.: Ground Penetrating Radar, 2nd edn. IEE, London (2004)CrossRefGoogle Scholar
  2. 2.
    Persico, R., Ludeno, G., Soldovieri, F., De Coster, A., Lambot, S.: Two-dimensional linear inversion of GPR data with a shifting zoom along the observation line. Remote Sens. 9(10), 980 (2017)CrossRefGoogle Scholar
  3. 3.
    Verma, P.K., Gaikwad, A.N., Singh, D., Nigam, M.J.: Analysis of clutter reduction techniques for through wall imaging in UWB range. Prog. Electromagn. Res. B 17, 29–48 (2009)CrossRefGoogle Scholar
  4. 4.
    Kumlu, D., Erer, I.: A comparative study on clutter reduction techniques in GPR images. In: International Conference on Electrical and Electronics Engineering, pp. 323–328 (2017)Google Scholar
  5. 5.
    Sharma, P., Kumar, B., Dingh, D., Gaba, S.P.: Critical analysis of background subtraction techniques on real GPR data. Def. Sci. J. 67(5), 559–571 (2017)CrossRefGoogle Scholar
  6. 6.
    Temlioglu, E., Erer, I.: Clutter removal in ground-penetrating radar images using morphological component analysis. IEEE Geosci. Remote Sens. Lett. 13(12), 1802–1806 (2016)CrossRefGoogle Scholar
  7. 7.
    Bao, Q.-Z., Qing-Chun, L., Wen-Chao, C.: GPR data noise attenuation on the curvelet transform. Appl. Geophys. 11(3), 301–310 (2014)CrossRefGoogle Scholar
  8. 8.
    Kumlu, D., Erer, I.: Multiscale directional bilateral filter based clutter removal techniques in GPR image analysis. In: IEEE International Geoscience Remote Sensing Symposium, pp. 2345–2348 (2017)Google Scholar
  9. 9.
    Hu, J., Li, S.: The multiscale directional bilateral filter and its application to multisensor image fusion. Inf. Fusion 13(3), 196–206 (2012)CrossRefGoogle Scholar
  10. 10.
    Guillemot, C., Cetin, A.E., Ansari, R: M-channel nonrectangular wavelet representation for 2-d signals: basis for quincunx sampled signals. In: Acoustics, Speech, and Signal Processing, pp. 2813–2816 (1991)Google Scholar
  11. 11.
    Ansari, R., Cetin, A.E., Lee, S.H.: Sub-band coding of images using nonrectangular filter banks. In: 32nd Annual Technical Symposium, pp. 315–323. International Society for Optics and Photonics (1988)Google Scholar
  12. 12.
    Kim, C.W., Ansari, R. : Subband decomposition procedure for quincunx sampling grids. In: SPIE Conference on Visual Comm. and Image Process, pp. 112–123 (1991)Google Scholar
  13. 13.
    Bozkurt, A., Suhre, A., Cetin, E.A.: Multi-scale directional filtering based method for follicular lymphoma grading. Signal Image Video Process. 8, 63–70 (2014)CrossRefGoogle Scholar
  14. 14.
    Belaid, S., Hattay, J., Naanaa, W., Aguili, T.: A new multi-scale framework for convolutive blind source separation. Signal Image Video Process. 10, 1203–1210 (2016)CrossRefGoogle Scholar
  15. 15.
    Lee, J.-C., Lo, T.-M., Chang, C.-P.: Dorsal hand vein recognition based on directional filter bank. Signal Image Video Process. 10, 145–152 (2016)CrossRefGoogle Scholar
  16. 16.
    Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Proceedings of the Sixth International Conference on Computer Vision, pp. 839–846 (1998)Google Scholar
  17. 17.
    Kaplan, N.H., Erer, I.: Bilateral filtering-based enhanced pansharpening of multispectral satellite images. IEEE Geosci. Remote Sens. Lett. 11(11), 1941–1945 (2014)CrossRefGoogle Scholar
  18. 18.
    Yaroslavsky, L.: Digital Picture Processing: An Introduction. Springer, Berlin (1985)CrossRefGoogle Scholar
  19. 19.
    Warren, C., Antonios, G., Iraklis, G.: gprMax: open source software to simulate electromagnetic wave propagation for ground penetrating radar. Comput. Phys. Commun. 209, 163–170 (2016)CrossRefGoogle Scholar
  20. 20.
    Temliolu, E., Erer, I., Kumlu, D.: A least mean square approach to buried object detection for ground penetrating radar. In: IEEE International Geoscience Remote Sensing Symposium, pp. 4833–4836 (2017)Google Scholar
  21. 21.
    Real GPR data, Vrije Univ. Brussel (VUB). Accessed on 01 Sep 2011 [Online]. http://www.minedet.etro.vub.ac.be

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Electronics and Communication Department, Faculty of Electrical and Electronics EngineeringIstanbul Technical UniversityIstanbulTurkey

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