Impaired GPR data reconstruction based on U-Net++

How to effectively reconstruct impaired ground penetrating radar (GPR) data has emerged as a crucial research topic for ensuring the detection accuracy. This paper presents a method for reconstructing impaired GPR data based on U-Net++. This method mainly consists of two stages: multi-scale structural similarity index (SSIM) missing positions detection and determination of U-Net++ reconstruction results by the maximum evaluation index method. This method is applicable regardless of whether the location and size of the missing area are known. U-Net++ is built upon the U-Net network structure, and the introduction of dense skip connections and nested subnetworks enhances the feature fusion and fine-grained detail recovery capabilities, thereby improving the reconstruction performance in complex environments. This paper systematically compiles a diverse dataset that includes simulation data and scanned data to conduct the experiment. The experimental results show that, compared with the original structure and some other variants of U-Net networks, U-Net++ achieves better reconstruction performance with approximately the same amount of time consumption. Compared with the interpolation algorithm reconstruction method, using U-Net++ for reconstruction has the advantages of shorter processing time and better results. This advantage is particularly evident in the reconstruction of large-size consecutive GPR trace missing. The method used in this article to determine the location and size of the missing area is currently only applicable to relatively simple scenarios. Further optimization is needed in future research.