Critical Analysis of Background Subtraction Techniques on Real GPR Data

Abstract

Ground penetrating radar (GPR) is used to detect the underground buried objects for civil as well as defence applications under varying conditions of soil moisture content. The capability of detection depends upon soil moisture, target characteristics and subsurface characteristics, which are mainly responsible for contaminating the GPR images with clutter. Researchers earlier have used averaging, mean, median, Eigen values, etc. for subtracting the background from GPR images. To analyse the background subtraction or clutter reduction problems, in this paper, we have experimentally reviewed background subtraction techniques with or without target conditions to enhance the target detection under variable soil moisture content. Indigenously developed GPR has been used to collect the data for different soil conditions and several background subtraction signal processing techniques were critically reviewed like, mean, median, singular value decomposition (SVD), principal component analysis (PCA), independent component analysis (ICA) and training methods. The signal to clutter ratio (SCR) measurement has been used for performance evaluation of each technique. The relative merits and demerits of each technique has also been analysed. The background subtraction techniques have been appliedto experimental GPR data and it is observed that in comparison of mean, SVD, median, ICA, PCA, the training method shows the highest SCR with buried target. Finally, this review helps to select the comparatively better background subtraction technique to enhance the detection capability in GPR.

Ground Penetrating Radar Weak Signals Denoising via Semi-soft Threshold Empirical Wavelet Transform

“…Many methods have been developed for random noise reduction and reflected events recovery. Recently, Timefrequency peak filtering method [2], singular value decomposition method [3], local signal-and-noise orthogonalization [4], and dictionary method [5], have been exclusively used for GPR data denoising. Because of the high SNR of GPR signals, it is easier to denoise.…”

Novel Adaptive Buried Nonmetallic Pipe Crack Detection Algorithm for Ground Penetrating Radar

“…It consists of a double-ridge horn antenna, a transceiver (TX/RX) unit, and a signal processing unit. Overall specifications of the GPR setup [18] and experimental parameters are listed in Table 1 Table 1(b), collected data have been denoted by (targets, Dataset' ). *Targets may be ground, target-1 (i.e., Without Cracked PVC pipe), target-2 (i.e., Cracked PVC pipe).…”

“…The soil moisture of field was varied from 5% to 15% in steps of 5% and burial depth of target varied from 15 cm to 50 cm in steps of 5 cm for Target-1 (non-cracked PVC pipe) and Target-2 (cracked PVC pipe). GPR A-scan and B-scan were carried out, details of which are given in [18]. The 1D GPR range profile is called as A-scan and when a number of A-scans are collected in the GPR data processing, which form a 2D matrix of a GPR image of the area of interest, it is known as B-scan.…”

Development of Adaptive Threshold and Data Smoothening Algorithm for GPR Imaging

“…where is the velocity of light = 3x10 8 m/s N is the number of frequency points = 631 f ∆ is the frequency interval between successive frequency points = 1.58 MHz (from Table 1 profile with background subtraction are shown with a number of reflection peaks 23 . As seen from A-scan profiles, clutter peaks behave like a target in without target A-scan profile, even after background subtraction techniques have been applied.…”