# Fusing Ground-Penetrating Radar Images for Improving Image Characteristics Fidelity
## 1 Introduction  
Significant progress has been made recently in the field of geophysical data analysis. Exploring and defining the subsurface using more potent and extensive approaches has been the driving force behind this advancement. To improve the process evaluation, different fusion approaches are applied to GPR data. The application of Hilbert-Huang transforms and empirical mode decomposition (EMD) to GPR data processing is the subject of [1]. To increase the signal quality and post-processing flexibility in 3D GPR imaging, the authors suggest a fusion algorithm method. The fusion strategy decreases disturbances in the relic positions and improves imaging accuracy by combining linear approaches with non-linear and non-stationary methods. The employment of two-dimensional wavelet transforms and singular value decomposition (SVD) for combining GPR and magnetic information is discussed in [2]. To improve the quality and accuracy of the data, especially for the detection of hidden structures, the study employs fusion techniques. The authors increase the understanding of the subsurface target by merging datasets from various geophysical techniques, which helps to discover previously unknown support structures. The authors in [3] present the use of forward and inverse S-transforms to fuse multi-frequency GPR data to detect background problems in railways. Therefore, they achieve deeper penetration and higher resolution using dual-frequency GPR profiling, which is crucial for detecting defects in railway infrastructure. The fusion methodology improves upon conventional assessment methods by synthesizing spectra to provide a composite image with enhanced imaging capabilities. The authors in [4] present a 3D GPR imaging system for subsurface object reconstruction and detection that is based on DNNs. This technology aims to accurately identify andvisualize underground objects using optical inertial fusion, improved migration techniques, and DNN-based detection of targets. The authors accomplish high-resolution imaging and precise depth estimates by combining multiple modules, including DepthNet, for depth prediction. The potential benefits of dual-polarization and ultra-wideband GPR for locating underground mines are considered in [5]. The study uses fusion techniques to minimize clutter and enhance target recognition in radar images using Mahalanobis-based algorithms. The authors provide high accuracy and low false alarm rates for real-time minefield identification by generating composite photos with improved contrast. In [6], how to merge InSAR and GPR datasets for the purpose of monitoring transportation infrastructure is described. The research highlights how data fusion can be used to detect deformation and subsidence in linear infrastructure, enabling detailed assessments and effective maintenance planning. The work in [7] presents the application of common histogram-oriented gradients (HOG) for landmine detection in GPR data. By combining HOG screening and embedded algorithms to achieve high detection rates, the study outperforms current feature extraction techniques. Furthermore, the combination of multi-frequency GPR data and their use in non-destructive testing is reported in [8]. Through numerical simulations and real-world investigations, the research demonstrates the deep penetration and high-resolution capabilities of fusion algorithms. To improve detection performance, ground-penetrating radar and metal detector algorithms are combined in [9] to investigate fusion decision processors for mine detection scenarios. By considering the statistical variations between the training and test data, the proposed strategy ensures optimal fusion performance in real-world situations. Curve transformation is explored in [10] for image fusion in archaeological applications. The fusion method enhances both the mapping and understanding of the archaeological site while still reducing ambient noise and highlighting the imprints of buried structures in geophysical images.