); [EMAIL_ADDRESS] (G.C. ); [EMAIL_ADDRESS] (H.L. ); [EMAIL_ADDRESS] (Y.L. ); [EMAIL_ADDRESS] (H.T. ); z3 [EMAIL_ADDRESS] (T.Z.) [PERSON]  
1 School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150006, China; [EMAIL_ADDRESS] (Y.Y.Y. ); [EMAIL_ADDRESS] (S.W. ); [EMAIL_ADDRESS] (G.C. ); [EMAIL_ADDRESS] (H.L. ); [EMAIL_ADDRESS] (Y.L. ); [EMAIL_ADDRESS] (H.T. ); z3 [EMAIL_ADDRESS] (T.Z.) [PERSON]  
1 School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150006, China; [EMAIL_ADDRESS] (Y.Y.Y. ); [EMAIL_ADDRESS] (S.W. ); [EMAIL_ADDRESS] (G.C. ); [EMAIL_ADDRESS] (H.L. ); [EMAIL_ADDRESS] (Y.L. ); [EMAIL_ADDRESS] (H.T. ); z3 [EMAIL_ADDRESS] (T.Z.) [PERSON]  
1 School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150006, China; [EMAIL_ADDRESS] (Y.Y.Y. ); [EMAIL_ADDRESS] (S.W. ); [EMAIL_ADDRESS] (G.C. ); [EMAIL_ADDRESS] (H.L. ); [EMAIL_ADDRESS] (Y.L. ); t593695287@163.  
other geoscience studies. In many application fields of GPR, a significant step of data processing is the detection and classification of buried objects and underground structures such as rebar, landmines, pipelines, bedrock, and underground voids. Although GPR has advantages in a relatively long distance of detection for the strong penetrating power of electromagnetic, it imposes an important drawback: the images produced by GPR are not as visually intuitive and high-resolution as some other techniques so that conventional manual operation requires skilled technicians and high cost of time. To reduce the difficulty of classification, some traditional image processing algorithms are utilized in the GPR data processing, such as background removal, filtering, and gain control. Except for those preprocessing methods, Hough transform (HT) has been widely used in hyperbola classification [3]. Then, as machine learning (ML) achieved promising results in many other fields, ML methods, including support vector machine (SVM), neural network (NN), hidden Markov model (HMM), K-nearest neighbors (K-NN), and dictionary learning (DL), have become a hotspot of GPR study. In the recent five years, convolutional neural network (CNN) and other deep learning algorithms have also been tentatively applied in the data processing of GPR system. Though there are still some problems in the practical application, it is commonly believed that those ML methods, instead of manual recognition, will be a trend of development in the recognition and classification of GPR targets. Although these methods have achieved good results in the detection and classification of underground structures, there are still several difficult problems to complete the automatic recognition of underground targets:  
* The selection of parameters in the process of image preprocessing still depends on manual work. Due to the complex structure of underground medium, there is a lot of clutter in the echo image of GPR. The plot scale parameter needs to be adjusted to enhance the target to make the target easier to detect. However, the optimal setting of this parameter is influenced by many factors, such as soil type and water content, and is very dependent on experience. It is difficult to set up automatically by computer. * There is a lack of standard datasets for GPR images. There is currently no standard dataset in the GPR field. The existing network models are trained by simulation data or GPR data collected by researchers themselves. The trained model may not be applicable to all soil conditions. * The amount of computation and computational complexity are high. Due to the large amount of clutter in GPR images, the computation is large and complex in the training process. How to reduce the complexity of the model while ensuring the detection performance is also a problem to be solved. [PERSON] et al. reviewed the artificial neural networks (ANN) and some other ML techniques applied to GPR before 2018 [4], and they listed the techniques from locating and testing to imaging and diagnosis.