# Fusing Ground-Penetrating Radar Images for Improving Image Characteristics Fidelity
## 1 Introduction
The authors in [11] highlight the correlation between the visual evaluation index (VEI) and GPR methods for the evaluation of concrete properties. Using a neural network model, the study establishes a connection between the two, allowing the creation of a fused NDE map for enhanced visualization. Similarly, ref. [12] describes the use of ground-penetrating radar data and deep learning techniques, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to identify buried explosive items. The work shows increased performance in their identification by comparing various fusion designs and techniques. The realization of the integration of hyperspectral and ground-penetrating radar (GPR) data for the purpose of calculating soil moisture is investigated by the authors in [13]. The accuracy is improved by combining different datasets with simulated GPR data significantly enhancing the estimation. However, it is emphasized that more research is needed to increase accuracy. For trying to find buried unexploded ordnance (UXO), the authors in [14] investigate a combination of electromagnetic induction (EMI) and GPR data. Compared to using GPR or EMI data alone, adding characteristics from both sensors using a boost classifier significantly improves clutter detection and rejection. This feature-level fusion method demonstrates how effectively fusing EMI and GPR data can improve target detection for buried UXOs. The authors in work [15] describe an innovative method to improve GPR imaging: super-resolution electromagnetic imaging (SREMI). It improves near-surface target detection and geologic structure imaging by creating sharper, higher-resolution GPR images by combining the slope and model sparsity through a fast iterative algorithm. Through the proposed method, the analysis is significantly enhanced, and artifacts are reduced. In conclusion, SREMI minimizes the differences between synthetic and observed data to obtain high-resolution subsurface permeability reflectivity images. Likewise, the study in [16] highlights the importance of the higher resolution made possible by high- and super-resolution GPR data for enabling in-depth interpretations that are necessary for cultural resource management, highlighting the need for new achievements in data processing. The authors in [17] address data discontinuity and sparse observation in on-vehicle multi-channel GPR data by calibrating each channel using an improved Sparse Blind Deconvolution (SBD) method and by applying a super-resolution method that interpolates zeros in between sparsely observed data and then applies synthetic aperture processing. In [18], sophisticated techniques for analyzing GPR data in archaeological settings are presented. These techniques, which include multi-attribute analysis and high-resolution topographic correction, have been successfully used to map buried archaeological features. In the processing of geophysical data, such as those obtained with the non-destructive method of ground-penetrating radar, it is considered important to extract and isolate the excessive footprints obtained by the antenna receiver. In computer vision and image processing [19; 20], feature extraction is often necessary with the aim of either discriminating between textures in an image due to its efficient pattern representation or object recognition based on the extracted features, but also in processes such as image segmentation and others [21; 22; 23]. The usefulness of the application of the Local Binary Pattern (LBP) algorithm has been observed, for example, in remote sensing data [24; 25], as well as in the excessive responses of the ground-penetrating radar system. It finds applications and can isolate the peaks of those in which it is likely to be located and which represent the true location of its target objects underground. So far, the need to develop advanced fusion techniques for geophysical data analysis has been highlighted, along with the variety of uses and approaches they have in many fields.