# Extraction of Hidden Information under Sootiness on MuraIs Based on Hyperspectral Image Enhancement
## 4 Comparison with Other Methods  
Interactive histogram stretching is a commonly used image enhancement method. The main stretching methods are linear, equalization, Gaussian, and square root. In order to compare the image enhancement effects of the method in this paper and the histogram stretching, all of these methods were applied to the original hyperspectral image of area 1 respectively. By adjusting the stretching range, the enhanced image with the best visual effect was obtained; the results are shown in Figure 9.  
The original hyperspectral image without MNF transform was directly subjected to image subtraction, and the result is shown in Figure 10. It can be seen that the significant scratch and noise band removal by MNF transform had a significant effect on image enhancement. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Threshold** & **Overall Accuracy (\%)** & **Omission Error (\%)** & **Commission Error (\%)** \\ \hline
6.30 & 85.72 & 6.36 & 7.92 \\
6.35 & 86.36 & 6.99 & 6.65 \\
6.40 & 86.87 & 7.64 & 5.49 \\
6.45 & 87.24 & 8.25 & 4.51 \\
6.50 & 87.44 & 8.91 & 3.65 \\
6.55 & 87.50 & 9.54 & 2.96 \\
6.60 & 87.41 & 10.18 & 2.41 \\
6.65 & 87.27 & 10.81 & 1.97 \\
6.70 & 86.97 & 11.41 & 1.62 \\
6.75 & 86.67 & 11.99 & 1.34 \\
6.80 & 86.37 & 12.52 & 1.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The accuracy of area 2 with different thresholds. Figure 9: The enhancement results of area 1 with different methods. (**a**) Enhancement results of the method in this paper; (**b**) results using the linear method; (**c**) results using linear 2%; (**d**) results using the Gaussian method; (**e**) results using the equalization method; (**f**) results using the square root method. In addition, blind separation technologies, such as principal component analysis (PCA) and independent component analysis (ICA), also proved to improve the readability of degraded images and reveal hidden information [23]. In order to compare the image enhancement effects of the method in this paper with that of PCA and ICA, these methods were applied to the original hyperspectral image of area 1. The bands with the most enhanced visual effects after PCA and ICA transform were given, and the results are shown in Figure 11.  
The results show that the method mentioned in this paper achieved better image enhancement effects. Image classification is a common method for feature extraction and recognition. For hyperspectral images, Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) are two commonly used classification methods. To compare the results obtained by the proposed method with those of SAM and SVM, both SAM and SVM were applied to the data. For SAM classification, the spectral curves of each area needed to be calculated first. The similarity between the two spectral curves was determined by calculating the generalized angel between the two curves; the smaller the angle, the more similar the two curves. The threshold was adjusted by multiple trials, and the visual effect was best when the thresholds were 0.025 for area 1 and 0.012 for area 2, respectively. The result of SAM is shown in Figure 12b,e. As for SVM, a very efficient machine learning algorithm, the basic idea was to find an optimal classification hyperplane that maximized the separation between the two types of areas. The result of SVM is shown in Figure 12c,f. Figure 11: The enhancement results of area 1 with different methods. (**a**) Enhancement results of the method in this paper; (**b**) the second band of principal component analysis (PCA) transform; (**c**) the fifth band of independent component analysis (ICA) transform. Figure 10: The enhancement results of area 1. (**a**) Subtraction of images transformed by MNF (in the spectral bands of 715–600 nm); (**b**) subtraction of original hyperspectral images without MNF (in the spectral bands of 715–600 nm). Compared to the two methods above, the proposed method in this paper provides better visual effects with higher accuracy.