## 1 Introduction
So far, the authors of published research works have not analyzed the direct influence of the quality of radargrams (that are characterized by a clear presence of usable signals) on the effectiveness of the detection or classification of underground objects. The application of automated algorithms increases the number of falsely detected objects, thus eliminating the detection of the signatures of underground objects, i.e., the characteristic hyperbolas. The key element in acquiring multiple images of the same area of interest is the assessment of their quality (in terms of the clear presence of usable signals). There are three types of image quality assessment (IQA) methods: full reference (FR) techniques that carry out assessment utilizing reference images, reduced reference (RR) techniquesrelated to comparing extracted features, and non-reference (NR) techniques that do not use any reference images [27; 28]. Full reference techniques enable the assessment of image quality in reference to a reference, i.e., the original image. These methods allow for the detection of areas that indicate distortions of the image. Reduced reference techniques consist in comparing selected properties of the image that were previously extracted from the reference image. Instead of the whole image, only important information is used in the analysis, which reduces the amount of data necessary to perform quality assessment. Finally, non-reference techniques do not require the use of reference images and are based on the identification of noisy areas and artifacts that prove the degradation of image quality. The traditional NR-IQA metrics are BLIINDS [29], NIQE [30], SSEQ [31], BRISQUE [32], and OG-IQA [33]. The significance of the use of blind-quality assessment was particularly emphasized in [34] by [PERSON], [PERSON] et al. However, the review of the existing literature does not reveal any studies on the analysis of images acquired with a ground-penetrating radar (both single- and multichannel) and on the selection of images of the highest quality. Object detection is the first and most fundamental step for the automatic analysis of visual information. The manual interpretation, detection, and analysis of underground objects in GPR images is a time-consuming process. There are many publications on the automated detection of hyperbolas that represent underground objects [11; 35] and on the detection and reconstruction of sub-surface hollow spaces [35; 36]. These methods usually employ deep learning [36; 37; 38]. The effectiveness of detection as explained by [PERSON], [PERSON] et al. in [36] was 90%. Although the detection results are promising, a disadvantage of deep learning algorithms is the fact that they require obtaining large sets of data that will be used in the equally time-consuming training process of the network. [PERSON], [PERSON] et al. in [39] used edge filters for the detection of hyperbolas. The issue of the automation of the detection and classification of underground objects has been the subject of research presented in multiple other studies [40; 41; 42; 43; 44; 45; 46; 47; 48]. [PERSON] in [49] noted, however, that a \"universal\" quality metric appears to be impossible: one application may use information of an image that is not useful to another application. There are only a few studies on the problem of the real objective quality evaluation for automatic analysis algorithms. The performance of automatic analysis methods relies on the quality of images that are processed. It is therefore essential to introduce objective metrics for predicting the quality of images evaluated by automatic analysis algorithms.