## 3 Results and Discussion
### Image Quality Assessment
#### 3.1.1 Overall Quality Assessment of Raw GPR Images
The entropy results for a sample measurement route were juxtaposed with the values of the parameters for assessing the effectiveness of detection, M and L, obtained for the channels indicated in the quality assessment. As for the entropy parameter for route 1, the selection of a higher number of channels in the 90% value criterion proves the existence of highly differentiated structures and details that are present in the analyzed images. Images with low entropy values (channels 11, 26, and 15) are characterized by the homogeneity and low diversity of structures. The selected images (Tables 2-4) characterized by low and high quality were the basis for generating quality assessment maps. Figures 7 and 8 present the quality assessment maps generated based on entropy for the selected images, for which extreme values of entropy were obtained, for three sample routes (routes 1 and 2--Figure 7, route 3--Figure 8). The yellow areas mark high levels of entropy (larger amount of information), while blue ones indicate low levels of entropy (potentially areas with a smaller amount of information or non-correlated noise level). High values of entropy are also a measure of the quality of the GPR signal. Areas characterized by low levels of entropy may indicate either a homogeneous ground medium or noise and the loss of GPR signal caused by higher attenuation of the ground medium. Areas characterized by lower entropy values may indicate a lower quality of data resulting from a smaller amount of information contained in the image. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Channel** & **32** & **27** & **29** & **24** &  
The quality of images selected with the entropy index was then assessed with the Laplacian variance and the PIQE indicator. Based on the obtained results (i.e., the images selected with two indicators: PIQE and the Laplacian variance), an intersection set of images was selected. The obtained selection results for three sample routes are presented in Figures 9-11 in form of Venn diagrams, respectively, for routes 1-3. The results presented in the Venn diagrams (Figures 9-11) enable the identification of images characterized by various levels of quality. Images selected based on the 90%  
Figure 8: Image quality assessment maps generated for the lowest and the highest values of entropy for route 3 (yellow areas correspond to the highest values of entropy). Figure 7: Image quality assessment maps generated for routes 1 and 2 for (**a,b**) the highest and (**c,d**) the lowest values of entropy (yellow areas correspond to the highest values of entropy). criterion (highest quality images) are marked in green, while images selected using the 10% criterion (lowest quality images) are marked in red, based on the indicators entropy, PIQE, and Laplacian variance. The analysis of the intersection of the sets represents the channels that ensure high quality based on all three indicators (e.g., Figure 10--channels 24 and 28). Images obtained based on two indicators point to channels that meet two criteria at the same time, e.g., entropy and Laplacian variance--channel 25 (Figure 11). The analysis of Figure 11 reveals that no channels were selected based on the criterion of 90% of value for the three indicators. In such events, one should select those channels that were determined based on entropy (i.e., channels 2-4)  
Figure 10: Venn diagram presenting the image selection results for route 2 based on the indicators entropy, PIQE, and Laplacian variance (green numbers indicate higher image quality, while red ones mark lower image quality). Figure 9: Venn diagram presenting the image selection results for route 1 based on the indicators entropy, PIQE, and Laplacian variance (green numbers indicate higher image quality, while red ones mark lower image quality). As for the entropy parameter (Figure 9), a higher number of channels was selected in the 90% criterion than for the PIQE indicator or for Laplacian variance.