Improved Tucker decomposition algorithm for noise suppression of 3D GPR data in road detection

Abstract

The echo data of ground‐penetrating radar (GPR) have a low signal‐to‐noise ratio. Denoising and interference suppression are important for improving the accuracy of underground target recognition and detection. In this paper, a new method of noise analysis and suppression of 3D GPR is proposed, transforming the problem of noise reduction into an optimization problem regarding a third‐order tensor. This method is improved with the following features; a bidimensional empirical mode decomposition (BEMD) algorithm is employed to decompose the 3D GPR noise. The main source of the noise is determined by computing the standard deviation of each component decomposed. For the shortcoming of the existing GPR denoising methods that reduce the dimension of 3D GPR data, the proposed approach can denoise 3D GPR data directly to avoid the loss of data caused by reducing the dimension. This preserves the desired signal and extracts noise through the Tucker decomposition of 3D GPR data. An improved high‐order orthogonal iteration algorithm is utilized to optimize the decomposition. The peak signal‐to‐noise ratio (PSNR) of the signal for each survey line is calculated to evaluate the effectiveness of noise reduction. Simulations and real data sets are provided to compare the performance of algorithms. The results show that the mean PSNR after noise suppression with our method is 8.915 dB and 16.458 dB higher than wavelet transform and singular value decomposition algorithms, respectively. This demonstrates that the proposed approach can better suppress 3D GPR noise and provides technical support for improving 3D GPR data quality and road detection accuracy.

Road detection and three-dimensional visualization management using deep learning and BIM integration

“…GPR has developed rapidly in recent years. It has the advantages of convenient detection, wide application range and intuitive detection results, and has become the main means of road underground disease detection at present [17,18]. However, the interpretation of road disease GPR images relies entirely on manual labour, which has low efficiency and long detection cycle, and is difficult to meet the growing needs of engineering [19,20].…”

Efficient Denoising of Multidimensional GPR Data Based on Fast Dictionary Learning

“…Efficient 3-D GPR data acquisition brings massive data information, with the multichannel data acquisition method of 3-D GPR bringing more abundant and complex noise sources. At present, there are few studies on the denoising processing of 3-D GPR data, so it is urgent to further explore the efficient denoising algorithm of 3-D GPR data [45], [46].…”