GPR data noise attenuation on the curvelet transform

Bao, Qianzong; Li, Qingchun; Chen, Wenchao

doi:10.1007/s11770-014-0444-2

Cited by 40 publications

(19 citation statements)

References 18 publications

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“…Önerilen yöntem literatürde var olan alt uzay tabanlı metodlar (TDA [5], TBA [5], BBA [5], MBA [9]) ve çoklu çözünürlük tabanlı metodlar (ED [11], ÇYKS [13]…”

Section: Sonuçlar Ve Tartişmalar (Results and Discussion)unclassified

“…Bu senaryo için algoritmaların tespit performansları Şekil 6'da AİK eğrileri ile gösterilmiştir. Elde edilen sonuçlar göstermektedir ki ÇYYOO, ÇYKS [13] ve MBA [9] yaklaşık olarak aynı performansı sergilemektedir, ikinci en iyi performans ise TDA [5] tarafından elde edilmiştir ve ED [11] ise onu takip etmektedir. Fakat TBA [5] nemli kum toprak, aluminyum hedef ve 2 cm gömülme derinliğidir.…”

Section: Sonuçlar Ve Tartişmalar (Results and Discussion)unclassified

“…Bu bilgiden yola çıkarak, literatürdeki bazı dönüşümler YNR görüntülerine uygulanmıştır [10]. Bunlar eğricik dönüşümü (ED) [11,12], çoklu-çözünürlüklü yönlü bilateral süzgeç (ÇYBS) yöntemi ve bu yönteme benzer fakat süre ve performans olarak daha iyi sonuç veren çoklu-çözünürlüklü yönlü komşuluk süzgeci (ÇYKS) dönüşümüdür [13]. Bu dönüşümler, resmi farklı ölçeklerde alt uzaylara böler ve bu alt uzaylara yaklaşım ve detay alt uzayları denir.…”

Section: Gi̇ri̇ş (Introduction)unclassified

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Yerel olmayan ortalamalar yaklaşımı ile yere nüfuz eden radar görüntülerinde kargaşa giderme

Kumlu¹,

Erer²

2020

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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Non-local means image decomposition  Image decomposition with directional filter banks  Construction of 2D simulated dataset by gprMax A novel clutter removal method based on non-local means (NLM) filtering for ground-penetrating radar (GPR) is presented. NLM filter which can be considered as a generalization of bilateral filter diverges from other local averaging filters since it determines the pixel weights by investigating the self-similarities in the image. NLM filter is extended to a multiscale-multidirectional version called multiscale directional non-local means (MDNLM) filter. Then, it is used to decompose the GPR image into approximation and detail subbands to capture the intrinsic geometrical structures of GPR image that contain both target and clutter information. After directional decomposition, the clutter is eliminated by keeping the diagonal information as target component. Finally, the inverse transform of the remaining subbands provides the reconstructed clutter-free GPR image. Figure A: The output images for detail and directional subbands of MDNLM method Purpose: The clutter removal methods in GPR images are as important as the GPR radar itself. Since, the detection rate is reported according to these results. Therefore, improvement in the ROC curves mean increase in the detection rate. The constructeed gprMax simulated data results show that NLM filter based method outperforms other state-of-the-art methods. Theory and Methods: The NLM method is extended as multi-scale form using "a trous wavelet transform" and multi-directional form using "directional filter banks" for GPR clutter removal implementation and the formulation is given in simple and concise way. Results: Both visual results and quantitative results are presented for simulated dataset and discussed in detail. In addition, visual results of the real dataset is provided for further analysis. The obtained results proved the superiority of our proposed method. Conclusion: A new GPR clutter removal method based on a multi-scale and multi-directional extension of NLM fillter is proposed. NLM has better performance to extract details and it is more robust to clutter hence outperform the other state of the art algorithms.

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“…Önerilen yöntem literatürde var olan alt uzay tabanlı metodlar (TDA [5], TBA [5], BBA [5], MBA [9]) ve çoklu çözünürlük tabanlı metodlar (ED [11], ÇYKS [13]…”

Section: Sonuçlar Ve Tartişmalar (Results and Discussion)unclassified

Section: Gi̇ri̇ş (Introduction)unclassified

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Yerel olmayan ortalamalar yaklaşımı ile yere nüfuz eden radar görüntülerinde kargaşa giderme

Kumlu¹,

Erer²

2020

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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“…Several methods have been applied to clutter suppression in GPR, and among them are clutter model-based methods [6][7][8], transform domain methods [9][10][11][12] and subspace projection methods [13][14][15][16][17]. Principal Component Analysis (PCA) is a major subspace projection method [14,15,[18][19][20][21][22], and the main problem of PCA is how to select the principal components of target.…”

Section: Introductionmentioning

confidence: 99%

A Clutter Suppression Method Based on Improved Principal Component Selection Rule for Ground Penetrating Radar

Zhu¹,

Xue²,

Xia³

et al. 2017

PIER M

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Abstract-Principal component analysis is usually used for clutter suppression of ground penetrating radar, but its performance is influenced by the selection of main components of target signal. In the paper, an improved principal component selection rule is proposed for selecting the main components of target signal. In the method, firstly difference spectrum of singular value is used to extract direct wave and strong target signal, and then, Fuzzy-C means clustering algorithm is used to determine the weights of principal component of weak target signal. Finally, the principal components of strong target signal and weak target signal are reconstructed to obtain target signal. Experimental results show that the proposed method can effectively remove the clutter signals and reserve more target information.

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“…These methods have been utilized for discrimination of plastic landmine and clutter object to reduce the false alarms (Savelyev et al 2007;Barkat et al 2000;Lopera et al 2007a;Zhou and Wang 2008). Time-frequency analysis has been also used for signal noise filtering and to improve signal to noise ratio (Weifeng and Mingxing 2010;Xu and Xu 2010;Zhou et al 2005;Bao et al 2014). Elizondo et al (2012) has studied the applicability of time-frequency analysis to GPR data using Wigner Ville Distribution and found increase in depth resolution for buried drum.…”

Section: Introductionmentioning

confidence: 99%

GPR Data Analysis of Weak Signals Using Modified S-Transform

2015

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GPR often encounters difficulty in visualizing the buried target when signals are weak and enveloped by noise, despite using the best of existing GPR data analysis tools. In this study, new method has been proposed based on modified S-transform to analyze weak signals of GPR data. The time-frequency analysis has been implemented to capture the change in electromagnetic waves reflected at the boundary of dielectric contrast. A series of experimental tests were conducted at different site conditions and with different materials using GSSI SIR 3000 GPR system with 400 MHz antenna. An improved 2-D vertical subsurface profile has been developed using time-frequency information to locate the target spatially as well as temporally. The new method has provided better results to improve the target visualization in case of noisy signals. However, it did not add any significant where conventional processing could do reasonable. The proposed method is convenient to use and capable of processing large number of scans effectively.

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GPR data noise attenuation on the curvelet transform

Cited by 40 publications

References 18 publications

Yerel olmayan ortalamalar yaklaşımı ile yere nüfuz eden radar görüntülerinde kargaşa giderme

Yerel olmayan ortalamalar yaklaşımı ile yere nüfuz eden radar görüntülerinde kargaşa giderme

A Clutter Suppression Method Based on Improved Principal Component Selection Rule for Ground Penetrating Radar

GPR Data Analysis of Weak Signals Using Modified S-Transform

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