Düzgün Olmayan Yönlü Süzgeç Bankası Yöntemi ile Yere Nüfuz Eden Radar Görüntülerinde Gürültü Giderme

Year 2020, Volume: 12 Issue: 2, 427 - 441, 30.06.2020

Şeyma Gündüz Günay , Deniz Kumlu

https://doi.org/10.29137/umagd.705100

Abstract

Yere nüfuz eden radar (YNR) engel arkası hedefleri görüntülemek için kullanılan önemli bir teknolojidir. Fakat çok geniş bantlı bir yapısı olmasından dolayı, alıcı tarafından toplanan sinyal gürültü içermektedir. Gürültü, hedef sinyalinin zayıf olmasından dolayı onu maskeleyebilir ve engel arkasındaki hedefin tespitini güçleştirir. Bu sebeple, YNR görüntülerinde gürültü giderme amacıyla birçok yöntem önerilmiştir. Bunlar arasında çok çözünürlüklü yöntemler önemli bir yer tutmaktadır. Bu yöntemlerden en bilineni ayrık dalgacık dönüşümü (ADD)’dür. ADD ile hedef çeşitli detay alt-bantlara ayrıştırılarak gürültü bileşeni eşikleme yöntemi ile giderilmeye çalışılır. Fakat bu yöntemde alt-bantlara ayrıştırma işlemi sınırlıdır ve bu yüzden performansı iyi değildir. ADD’den sonra, daha kapsamlı bir ayrıştırma yapan eğricik dönüşümü (ED) gürültü giderme amacı ile YNR görüntülerinde uygulanmış ve iyi bir performans sergilemiştir. Fakat ED yönteminde frekans bölgesinde sabit bir bölümleme işlemi yapılmaktadır. Bu çalışmada önerilen düzgün olmayan yönlü süzgç bankası (DOYSB)’ında ise, ED gibi görüntüyü sabit frekans aralıklarına bölmek yerine esnek bir yapıda frekans bölme imkanı sağlamaktadır. Bu sayede, YNR görüntüsünde hedefi daha iyi koruyabilecek frekans bölgesi ayrışımı yapılabilmektedir ve gürültü daha efektif olarak hedef sinyalini bozmadan giderilmektedir. Önerilen DOYSB yöntemi, mevcut yöntemler ile zorlayıcı bir simülasyon veri seti kullanılarak karşılaştırılmıştır. DOYSB yönteminin üstünlüğü hem görsel hem sayısal olarak gösterilmiştir.

Keywords

Yere nüfuz eden radar, gürültü giderme, ayrık dalgacık dönüşümü, eğricik dönüşümü, düzgün olmayan yönlü süzgeç bankası dönüşümü

Supporting Institution

-

Project Number

-

Thanks

-

Ground Penetrating Radar Image Denoising via Nonuniform Directional Filter Bank Method

Abstract

Ground penetrating radar (GPR) is an important technology that is used for imaging target through obstacle. Since it has ultrawide band structure, the signal obtained by receiver contains noise component. This component can corrupt the target signal and make target detection difficult. Thus, there are numerious methods are proposed for denoising purposes in GPR community and multiresolution based methods are one of them. The most popular one is the discrete wavelet transform (DWT) where it decomposes the GPR image into detail subbands then the obtained wavelet coeffcients are thresholded to remove the noise. However, DWT decomposition is very limited and the performance is not satisfying in GPR denoising. Then, the curvelet transform (CT) is proposed which makes more detailed decomposition compared to DWT thus it obtained better results. However, the frequency partitioning of the CT is fixed and it cannot be changed. In this study, we proposed a nonuniform directional filter bank (NUDFB) which has arbitrary frequency partitioning unlike CT. Thus, it gives us ability to make more effective frequency partitioning by preserving the target signal structure and the noise component can be remove more effecitiently. The proposed NUDFB method is compared with the other avaiable methods by using our simulated dataset which contains challenging scenarios. Both visual and quantitative results which obtained for the simulated dataset are proved the superiorty of our proposed method.

Keywords

“Ground penetrating radar, denoising, discrete wavelet transform, curvelet transform, nonuniform directional filter bank

Project Number

-

References

• Abujarad, F., Jostingmeier, A., & Omar, A. S. (2004, June). Clutter removal for landmine using different signal processing techniques. In Proceedings of the Tenth International Conference on Grounds Penetrating Radar, 2004. GPR 2004. (pp. 697-700). IEEE.
• Averbuch, A., Coifman, R. R., Donoho, D. L., Elad, M., & Israeli, M. (2006). Fast and accurate polar Fourier transform. Applied and computational harmonic analysis, 21(2), 145-167. doi: 10.1016/j.acha.2005.11.003.
• Baili, J., Lahouar, S., Hergli, M., Al-Qadi, I. L., & Besbes, K. (2009). GPR signal de-noising by discrete wavelet transform. Ndt & E International, 42(8), 696-703. doi: 10.1016/j.ndteint.2009.06.003.
• Baili, J., Lahouar, S., Hergli, M., Amimi, A., & Besbes, K. (2006). Application of the discrete wavelet transform to denoise GPR signals. In 2nd International Symposium on Communications, Control and Signal Processing, Marrakech, Morocco (p. 11).
• Bao, Q. Z., Li, Q. C., & Chen, W. C. (2014). GPR data noise attenuation on the curvelet transform. Applied Geophysics, 11(3), 301-310.
• Brunzell, H. (1999). Detection of shallowly buried objects using impulse radar. IEEE Transactions on Geoscience and Remote sensing, 37(2), 875-886. doi: 10.1109 / 36.752207.
• Candes, E., Demanet, L., Donoho, D., & Ying, L. (2006). Fast discrete curvelet transforms. Multiscale Modeling & Simulation, 5(3), 861-899. doi: 10.1137/05064182X.
• Chen, X. Y., Xie, X. M., & Shi, G. M. (2006, May). Direct design of near perfect reconstruction linear phase nonuniform filter banks with rational sampling factors. In 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings (Vol. 3, pp. III-III). IEEE. doi: 10.1109/ICASSP.2006.1660638.
• Da Cunha, A. L., Zhou, J., & Do, M. N. (2006). The nonsubsampled contourlet transform: theory, design, and applications. IEEE transactions on image processing, 15(10), 3089-3101. doi: 10.1109/TIP.2006.877507.
• Donoho, D. L. (1995). De-noising by soft-thresholding. IEEE transactions on information theory, 41(3), 613-627. doi: 10.1109 / 18.382009.
• Engin, M. A., & Çavuşoğlu, B. (2014, April). Curvelet transform based image denoising via Gaussian mixture model. In 2014 22nd Signal Processing and Communications Applications Conference (SIU) (pp. 1499-1502). IEEE. doi: 10.1109/SIU.2014.6830525
• Fang, L., Ye, L., Tie, Y., Zhong, W., & Zhang, Q. (2018). Design of linear-phase nonsubsampled nonuniform directional filter bank with arbitrary directional partitioning. Journal of Visual Communication and Image Representation, 51, 23-28. doi: 10.1016/j.jvcir.2017.12.013.
• Gebremichael, T., Mali, D., & Zoubir, A. M. (2011, July). Clutter reduction techniques for GPR based buried landmine detection. In 2011 International Conference on Signal Processing, Communication, Computing and Networking Technologies (pp. 182-186). IEEE. doi: 10.1109/ ICSCCN.2011.6024540.
• Gupta, D., & Choubey, S. (2015). Discrete wavelet transform for image processing. International Journal of Emerging Technology and Advanced Engineering, 4(3), 598-602.
• Kumlu, D., & Erer, I. (2017, July). Multiscale directional bilateral filter based clutter removal in GPR image analysis. In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 2345-2348). IEEE. doi: 10.1109 / IGARSS.2017.8127461.
• Kumlu, D., & Erer, I. (2017, April). A comparative study on clutter reduction techniques in GPR images. In 2017 4th International Conference on Electrical and Electronic Engineering (ICEEE) (pp. 323-328). IEEE. doi: 10.1109/ICEEE2.2017.7935843.
• Kumlu, D.(2018). New Clutter Removal Methods For Through Obstacle Target Detection, Doktora Tezi, İstanbul Teknik Üniversitesi, İstanbul, Türkiye.
• Liang, L., Shi, G., & Xie, X. (2010). Nonuniform directional filter banks with arbitrary frequency partitioning. IEEE transactions on image processing, 20(1), 283-288. doi: 10.1109/TIP.2010.2052267.
• LIU, Hanzhou; GUO, Baolong; FENG, Zongzhe. Pseudo-log-polar Fourier transform for image registration. IEEE Signal Processing Letters, 2005, 13.1: 17-20. doi: 10.1109/LSP.2005.860549.
• Park, C. H., Lee, J. J., Smith, M. J., Park, S. I., & Park, K. H. (2004). Directional filter bank-based fingerprint feature extraction and matching. IEEE Transactions on Circuits and Systems for Video Technology, 14(1), 74-85. doi:10.1109/TCSVT.2003.818355.
• Peng, W., Hongling, X., & Pengcheng, X. (2016). Research on ground penetrating radar image denoising using nonsubsampled contourlet transform and adaptive threshold algorithm. International Journal of Signal Processing, Image Processing and Pattern Recognition, 9(5), 219-228.
• Rosiles, J. G., & Smith, M. J. (2001, May). Texture classification with a biorthogonal directional filter bank. In 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No. 01CH37221) (Vol. 3, pp. 1549-1552). IEEE. doi: 10.1109/ICASSP.2001.941228.
• Starck, J. L., Candès, E. J., & Donoho, D. L. (2002). The curvelet transform for image denoising. IEEE Transactions on image processing, 11(6), 670-684.
• Terrasse, G., Nicolas, J. M., Trouvé, E., & Drouet, É. (2015, July). Application of the curvelet transform for pipe detection in GPR images. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 4308-4311). IEEE. doi: 10.1109 / IGARSS.2015.7326779.
• Terrasse, G., Nicolas, J. M., Trouvé, E., & Drouet, E. (2017). Application of the Curvelet Transform for clutter and noise removal in GPR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(10), 4280-4294. doi: 10.1109 / JSTARS.2017.2717960.
• Tjora, S., Eide, E., & Lundheim, L. (2004, June). Evaluation of methods for ground bounce removal in GPR utility mapping. In Proceedings of the Tenth International Conference on Grounds Penetrating Radar, 2004. GPR 2004. (Vol. 1, pp. 379-382). IEEE.
• Tomasi, C., & Manduchi, R. (1998, January). Bilateral filtering for gray and color images. In Sixth international conference on computer vision (IEEE Cat. No. 98CH36271) (pp. 839-846). IEEE. doi: 10.1109 / ICCV.1998.710815.
• Wang, X. N., & Liu, S. X. (2016, June). Noise suppressing and direct wave removal in GPR data based on shearlet transform. In 2016 16th International Conference on Ground Penetrating Radar (GPR) (pp. 1-5). IEEE. doi: 10.1109/ICGPR.2016.7572615.
• Warren, C., & Giannopoulos, A. (2011). Creating finite-difference time-domain models of commercial ground-penetrating radar antennas using Taguchi’s optimization method. Geophysics, 76(2), G37-G47. doi: 10.1190/1.3548506.