## 3 Results and Discussion
### Image Quality Assessment
#### 3.1.1 Overall Quality Assessment of Raw GPR Images  
In the first, rough assessment of GPR image quality, the total number of features detected in the analyzed radargrams was presented (M\({}_{2}\)--the number of objects detected in the output image (i.e., after pre-processing))--Step 1 in Figure 3. The number of objects (M\({}_{2}\)) was determined based on the image obtained from the binarization process. Figure 5 presents the number of objects detected in images acquired from three measurement routes. Looking closely at Figure 5, one may notice that in this case, images characterized by a higher number of detected features were obtained for the selected channels. Due to the ultimate goal being the detection of underground infrastructure in radargrams, the  
Figure 5: Number of objects detected in images acquired on (**a**) route 1, (**b**) route 2, and (**c**) route 3.  
authors have proposed the adaptive approach and a further detailed assessment of image quality (Section 3.1.2). Figure A1 in Appendix A presents the number of objects detected in images acquired from routes 1-3, with the specification of channels selected from the quality assessment in Section 3.1.2. 1.2 Assessment of the Quality of GPR Images for the Detection of Hyperbolas Representing Underground Utilities  
In the process of our research work, we tested the following coefficients: NIQE, BRISQUE, PIQE, entropy, and Laplacian variance. Due to the quantitative limitations of this article, we did not include the results obtained from all coefficients. Table A1 (Appendix A) adds the values of the aforementioned coefficients determined for the images from the three measurement routes (routes 1-3). Based on Table A1 (Appendix A), it can be noted that there is no correlation between the values of the coefficients obtained from NIQE and BRISQUE and the coefficients determining the effectiveness of hyperbola detection (M, L). After analyzing the values of the obtained coefficients and the correlation between the image quality assessment coefficients and the effectiveness of hyperbola detection, three image quality assessment coefficients were selected for further analysis and image selection: entropy, Laplacian variance, and PIQE. However, the use of NIQE and BRISQUE coefficients in the adaptive radargram selection method was rejected. The results of the quality assessment indicators (entropy, PIQE, and Laplacian variance) and of the effectiveness of hyperbola detection (M and L) for a sample measurement route (route 2) are presented in Figure 6. The results of these indicators for the two remaining measurement routes (routes 1 and 3) are presented in Appendix A, Figure A2 (route 1), and Figure A3 (route 3). The images acquired on route 2 (Figure 6) show the lowest values of entropy in comparison to routes 3 (Appendix A--Figure A3) and 1 (Appendix A--Figure A2), which may prove that the image contains a larger amount of information. The lowest values of  
Figure 6: Results of the quality assessment indicators (entropy, PIQE, and Laplacian variance) and the effectiveness of the detection of hyperbolas (M, L) obtained for route 2 based on (**a**) entropy, (**b**) PIQE and (**c**) Laplacian variance. entropy were obtained for images acquired from channels 3, 7, 11, 15, 19, and 23. Higher values obtained for routes 1 and 2 point to a higher degree of noise and heterogeneity of data. The highest values of PIQE were obtained for images acquired from channels 1, 6, 8-10, 12, 16, 19-21, and 24-32. The highest value of Laplacian variance was obtained for the image acquired from channel 24. Images acquired on route 3 are characterized by a lower degree of blurring than those acquired on routes 1 and 2. Tables 2-4 present the values of entropy obtained for sample measurement routes (respectively, for routes 1, 2, and 3) for the images of the lowest and the highest quality, obtained based on the proposed selection criteria of 10% and 90%.