## 2 Materials and Methods
### Adaptive Method of Radargram Quality Assessment  
The image quality assessment took into account the influence of the degree of blurring, noise, and the structures and details that were present in the analyzed images. The assessment also took into consideration the indicators that operate on whole radargrams (Laplacian variance, entropy) and on specific blocks of the image of a predefined size, e.g., 8 \(\times\) 8, 16 \(\times\) 16, or 32 \(\times\) 32 (perception-based image quality evaluator--PIQE). The flow chart of the proposed method for image quality assessment is provided in Figure 4.  
The first stage after the pre-processing of radargrams is the assessment of the quality of all acquired images with the entropy indicator. Quality assessment maps were generated for the analyzed images. Then, the maps were superimposed on the pre-processed image, which enabled the visualization of the quality distribution within the whole radargram. The first, general verification of the quality indicators in the later process of the automated detection of features (hyperbolas) used the total number of features detected (M\({}_{2}\)--the number of objects detected in the output image (i.e., after pre-processing)--Step 1 in  
Figure 4: Flowchart of the proposed method for assessing image quality. Figure 3). At this stage, the initial assumption/thesis stating that the overall quality of the image has a direct influence on the number of detected features was confirmed. Due to the aim being the detection of underground infrastructure in radargrams, the authors have proposed the adaptive approach and a further detailed assessment of image quality. This is why in the second step, the images acquired from channels characterized by the highest and lowest image quality were designated based on the selected criteria that define the threshold for the selection of images that are characterized by only the boundary values of GPR data quality. This process enabled the elimination of data of the lowest quality. For the purposes of this analysis of the boundary conditions, a threshold of statistical significance at the level of \(\alpha=0.1\) was assumed. Based on this criterion (\(\alpha=10\%\) and, respectively, \(1-\alpha=90\%\)), images of the lowest quality (\(10\%\)) and of the highest quality (\(90\%\)) were designated. The final selection of a threshold of \(0.10\) was dictated by the limited dataset. With a threshold of \(0.02\) or \(0.05\), it would have been impossible to extract significant qualitative features from the images. In addition to theoretical considerations, the threshold value was chosen experimentally. The authors tested a variant with a threshold of \(0.02\) and \(0.05\), but the results indicated that this was too strict an approach that would make it impossible to capture and analyze the qualitative changes occurring in the GPR images. The aim of the proposed criterion (\(\alpha=0.1\)) for image selection is to eliminate overly similar images. At the second stage, in the group of images selected based on entropy, image quality was assessed (in terms of blur and noise) with Laplacian variance and the PIQE indicator. The obtained values of the Laplacian variance and PIQE indicator were the basis for selecting the channels of the highest quality. Based on the images selected with the use of these indicators, a set of common elements, being one or several images, was defined. Due to the introduced selection criterion, there is no specific number of the resultant number of channels from which the images were acquired. In some cases, the user may obtain \(1\), \(2\), \(3\), or more images. If only one or two channels are obtained, the analysis should be expanded to include a selection of at least three channels. This process will ensure the possibility of verifying the detected underground objects in a minimum of three images. This stage will provide additional verification of the detected objects that are not elements of underground utility networks.