Ground Penetrating Radar : Theory and Applications

BasicMethods for c-Means Clustering- Variations and Generalizations - I- Variations and Generalizations - II- Miscellanea- Application to Classifier Design- Fuzzy Clustering and Probabilistic PCA Model- Local Multivariate Analysis Based on Fuzzy Clustering- Extended Algorithms for Local Multivariate Analysis

Algorithms for fuzzy clustering : methods in c-means clustering with applications

Low complexity clutter removal in GPR images via lattice filters

Ground-penetrating radar (GPR), as a non-invasive instrument, has been widely used in civil engineering. In GPR B-scan images, there may exist random noise due to the influence of the environment and equipment hardware, which complicates the interpretability of the useful information. Many methods have been proposed to eliminate or suppress the random noise. However, the existing methods have an unsatisfactory denoising effect when the image is severely contaminated by random noise. This paper proposes a multi-scale convolutional autoencoder (MCAE) to denoise GPR data. At the same time, to solve the problem of training dataset insufficiency, we designed the data augmentation strategy, Wasserstein generative adversarial network (WGAN), to increase the training dataset of MCAE. Experimental results conducted on both simulated, generated, and field datasets demonstrated that the proposed scheme has promising performance for image denoising. In terms of three indexes: the peak signal-to-noise ratio (PSNR), the time cost, and the structural similarity index (SSIM), the proposed scheme can achieve better performance of random noise suppression compared with the state-of-the-art competing methods (e.g., CAE, BM3D, WNNM).

GPR B-Scan Image Denoising via Multi-Scale Convolutional Autoencoder with Data Augmentation

In detecting shallow buried underground objects, reflected power from ground, i.e. ground surface clutter makes the task extremely difficult. In order to remove ground clutter, conventional methods in the literature are not as much effective as we need for objects buried detection in shallow depths. In this paper, a robust method, based on Time gating and wiener filtering, is proposed, which is very precise and effective in Ultra wideband (UWB) imaging. The problem with time gating method solely is that the timing window length for unknown target depths cannot be determined beforehand with sufficient accuracy. Imprecise window length selection removes parts of target signals along with the clutter and increases missed detection probability. This paper proposes an algorithm to circumvent this problem by first using a wiener filter for cancellation of ground clutter to a reasonable extent and pre detection of target positions by average similarity function (ASF). The time gating method is then used in the second step using the information provided from the first step for window length selection. The combination of the two steps provides better detection of shallow buried objects with less missed detection of targets.

Robust Wiener filter-based time gating method for detection of shallowly buried objects

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.

and Yunyun Yu

Papers

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