# Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data
## 3 Results
### Classification Results  
In this experiment, using the proposed method, the Pol-SAR images can be classified. First, the data should be pre-processed. A refined Lee filter with 9 \(\times\) 9 window size is chosen to reduce the noise. Then, after superpixel segmentation, 1800 superpixels remain, and the result is shown in Figure 8. It can be seen that most pixels belonging to the same superpixel have nearly the same color. This means that these pixels have nearly the same scattering mechanism, and can be classified as one group. Then, polarimetric features can be extracted from the Pol-SAR data, and the features maps are shown in Figure 9. It can be seen that when just using the original features, some classes can not be distinguished, however significant differences can be found in the features maps between these two classes when the new features are used. Finally, extracted features are entered into the SSAE which has three hidden layers with 60, 80, and 100 nodes, respectively. The classified map is depicted in Figure 10. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Class Label** & **All Pixels** & **Training Pixels** & **Testing Pixels** \\ \hline
**Alluvium** & 1 & 343,987 & 17,199 & 326,788 \\
**Beitashan Formation** & 2 & 322,833 & 16,142 & 306,691 \\
**Diluvium** & 3 & 167,735 & 8,387 & 159,348 \\
**Biotite granite, Leucogranite** & 4 & 211,923 & 10,596 & 201,327 \\
**Upper Aeramantie Formation** & 5 & 318,968 & 15,948 & 303,020 \\
**Alluvium–Diluvium** & 6 & 371,692 & 18,585 & 353,107 \\
**Nanmingshui Formation** & 7 & 352,351 & 17,618 & 334,733 \\
**Mayinebo Formation** & 8 & 212,628 & 10,631 & 208,375 \\
**Total** & - & 2,302,117 & 115,106 & 2,187,011 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Training and testing pixel numbers for the Qinghe dataset. Figure 7: Changing of the OA with the rate of training samples. _Appl. Sci._**2018**, \(8\), 1513  
Figure 8: Superpixel segmentation result. To verify the advantages of the new features compared to the original features (\(\text{P}_{\text{s}\_i}\),\(\text{P}_{\text{v}\_i}\),\(\rho_{\text{hh}\_vv\_i}\), and \(r_{\text{sv}\_i}\) (\(i=\text{L}\), C)), comparison experiments and analysis are conducted. In this experiment, we use \(d_{i,j}\), which is defined as follows, to describe the difference between class \(i\) and \(j\):  
\[d_{i,j}=\max\left\{\frac{\tilde{f}_{i}}{\tilde{f}_{j}},\frac{\tilde{f}_{j}}{ \tilde{f}_{i}}\right\}, \tag{25}\]  
where \(\tilde{f}_{i}\) and \(\tilde{f}_{j}\) represent the average of class \(i\) and \(j\) with feature \(f\), respectively. \(d_{i,j}\) is a number greater than 1, and it is proportional to the difference between two classes. From Figures 3 and 4, we can directly see that Beitashan Formation (class 2) and Upper Aermantie Formation (class 5) are difficult to distinguish, Diluvium (class 3) is easily confused with Alluvium-Diluvium (class 6). Therefore, \(d_{2,5}\) and \(d_{3,6}\) are calculated in the regions marked in Figure 9a with original features and new features. As can be seen in Table 3, \(d_{2,5}\) and \(d_{3,6}\) for the new features are greater than those for the original features, which indicates that the new features are more effective in increasing the class separability than the original features. For example, for the situation that just uses \(r_{\text{x}\_L}\), \(d_{3,6}\) is 1.06. However, \(d_{3,6}\) is improved to 1.31 when \(\Delta r_{\text{x}\_N}\) is used. Figure 9: Features maps. (**a**) Scattering power of the surface in the C-band. (**b**) Scattering power of the surface in the L-band. (**c**) The changing trend of the scattering power of the surface. (**d**) Scattering power of the volume in the C-band. (**e**) Scattering power of the volume in the L-band. (**f**) The changing trend of the scattering power of the volume. (**g**) Co-polarized correlation coefficient of the C-band. (**h**) Co-polarized correlation coefficient of the L-band. (**i**) The changing trend of co-polarized correlation coefficient. (**j**) Cross polarized ratio of the C-band.