# Extraction of Hidden Information under Sootiness on MuraIs Based on Hyperspectral Image Enhancement
## 3 Results and Discussion
### Accuracy Evaluation  
The image subtraction was used again to evaluate the accuracy of the extraction results. The difference images of the two areas in Figure 8c,f were obtained by subtracting the density slicing image (shown in Figure 8b,e) with the test images (shown in Figure 8a,d). The pattern was determined after the image enhancement, and the test images were drawn manually on the enhanced images (Figure 5c) with ROI using ENVI software. As shown in Table 1, in the difference image of area 1 with a threshold of 7.95, the black area shows the correctly extracted pixels, and the overall accuracy reached 88.97%. The omission error shown in orange accounted for 6.68%, while the commission error shown in green accounted for 4.35%. In the difference image of area 2 with a threshold of 6.55, the overall accuracy was 87.50%. The omission error accounted for 9.54%, while the commission error accounted for 2.96%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Area** & **Overall Accuracy (\%)** & **Omission Error (\%)** & **Commission Error (\%)** \\ \hline Area 1 & 88.97 & 6.68 & 4.35 \\ Area 2 & 87.50 & 9.54 & 2.96 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The overall accuracy of the two areas.  
Figure 7: The extraction of the hidden information using density slicing: (**a**) Color image of area 1 (synthesized from original hyperspectral images); (**b**) the DN value of area 1 is transformed to start from 0 and the pattern is highlighted in white; (**c**) the pattern of area 1 is extracted by density slicing with threshold of 7.95; (**d**) color image of area 2; (**e**) the DN value of area 2 is transformed to start from 0 and the pattern is highlighted in white; (**f**) the pattern of area 2 is extracted by density slicing, with a threshold of 6.55.  
During the experiments, it was found that the setting of the value range affected the extraction results greatly. An analysis test was done to set the threshold to achieve the best effect of pattern extraction. The comparison of accuracy of area 1 with different thresholds is shown in Table 2. From 7.75 to 8.25, the omission error was gradually increased and the commission error was gradually reduced. The overall accuracy reached the maximum of 88.97% at 7.95.  
The accuracy of area 2 with different thresholds is shown in Table 3. Similarly, from 6.30 to 6.80, the omission error was gradually increased and the commission error was gradually reduced. When the threshold was 6.55, the overall accuracy was at its highest, reaching 87.50%.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Threshold** & **Overall Accuracy (\%)** & **Omission Error (\%)** & **Commission Error (\%)** \\ \hline
7.75 & 88.26 & 4.90 & 6.84 \\
7.80 & 88.59 & 5.29 & 6.12 \\
7.85 & 88.8 & 5.74 & 5.46 \\
7.90 & 88.94 & 6.18 & 4.88 \\
7.95 & 88.97 & 6.68 & 4.35 \\
8.00 & 88.94 & 7.19 & 3.87 \\
8.05 & 88.86 & 7.68 & 3.46 \\
8.10 & 88.68 & 8.22 & 3.10 \\
8.15 & 88.47 & 8.76 & 2.77 \\
8.20 & 88.22 & 9.30 & 2.48 \\
8.25 & 87.92 & 9.85 & 2.23 \\ \hline \end{tabular}
\end{table}
Table 2: The accuracy of area 1 with different thresholds.  
Figure 8: The extraction results of the hidden information of (**a–c**) area 1 and (**d–f**) area 2 using classification methods. (**a,d**) Test image drawn manually; (**b,e**) extraction results of density slicing; (**c,f**) difference image generated by subtracting the density slicing image from the test image.