# Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data
## 3 Results
### Comparison with Other Classifiers
#### 3.3.1 Classification Accuracy  
To validate the lithology classification accuracy, a comparison between the proposed method and other methods is made in this section. The classifiers compared are three SSAE methods with different \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**RealPredicted** & **Class 1** & **Class 2** & **Class 3** & **Class 4** & **Class 5** & **Class 6** & **Class 7** & **Class 8** \\ \hline Class 1 & 306,036 & 0 & 155 & 0 & 187 & 1,767 & 1,413 & 30 \\ Class 2 & 205 & 286,749 & 0 & 21 & 889 & 3 & 2,682 & 0 \\ Class 3 & 0 & 0 & 147,703 & 60 & 0 & 3,198 & 0 & 0 \\ Class 4 & 0 & 0 & 49 & 189,009 & 1,436 & 28 & 208 & 0 \\ Class 5 & 0 & 191 & 795 & 307 & 285,751 & 27 & 0 & 0 \\ Class 6 & 0 & 18 & 6,420 & 0 & 221 & 327,856 & 7 & 0 \\ Class 7 & 0 & 0 & 0 & 204 & 0 & 406 & 316,459 & 46 \\ Class 8 & 0 & 0 & 0 & 0 & 0 & 38 & 83 & 119,343 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion Matrix. Figure 10: Classified map based on the proposed method. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \(\mathbf{P_{s,L}}\) & \(\mathbf{P_{s,C}}\) & \(\Delta\mathbf{P_{s}}\) & \(\mathbf{P_{v,L}}\) & \(\mathbf{P_{v,C}}\) & \(\Delta\mathbf{P_{v}}\) & \(\rho\_{c}\) & \(\rho\_{L}\) & \(\Delta\rho\) & \(\mathbf{r_{sv,C}}\) & \(\mathbf{r_{sv,L}}\) & \(\Delta\mathbf{r_{sv}}\) \\ \hline \(d_{2,5}\) & 1.08 & 1.06 & **1.26** & 1.03 & 1.08 & **1.21** & 1.01 & 1.03 & **1.20** & 1.02 & 1.06 & **1.31** \\ \(d_{3,6}\) & 1.00 & 1.03 & **1.10** & 1.02 & 1.15 & **1.18** & 1.01 & 1.01 & **1.04** & 1.03 & 1.11 & **1.27** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The difference between two classes. inputs, the Support Vector Machine (SVM) classifier, and [PERSON]'s method from Reference [18]. The three SSAE methods in this paper can be labeled as M1, M2, and M3, whose inputs are the coherency matrix of the L-frequency, the C-frequency, and the dual-frequency Pol-SAR data, respectively. Both the SVM classifier and [PERSON]'s method utilized the same features as the proposed method. The superpixel segmentation is introduced for these comparison methods. The SVM is a typical method in classification and [PERSON]'s method is a state of the art method in Pol-SAR image classification. In the experiment of SVM, 'rbf' is used as the kernel and c = 100, gamma = 0.01. The classification accuracy is reported in Table 5 and the results are plotted in Figure 11. The OA and kappa coefficient (Kappa) are calculated to evaluate the classification performance. Kappa is defined as:  
\[\text{Kappa}=\frac{N\sum\limits_{i=1}^{r}x_{ii}-\sum\limits_{i=1}^{r}\left(x_{i +}\times x_{+i}\right)}{N^{2}-\sum\limits_{i=1}^{r}\left(x_{i+}\times x_{+i} \right)}, \tag{26}\]  
where \(N\) is the number of total samples, \(r\) denotes the number of classes, \(x_{ii}\) is the diagonal elements in confusion matrix, and \(x_{i+}\) and \(x_{+i}\) are the number of samples in the \(i\)th row and the \(i\)th column of confusion matrix, respectively. It can be directly seen from Figure 11 that the classification performance is improved significantly compared with the other five methods. As shown in Table 5, the proposed method has a lithology classification accuracy of 98.90% in this experiment, and it outperforms M1, M2, M3, SVM, and [PERSON]'s method, which have accuracies of 72.99%, 80.55%, 94.70%, 86.69%, and 80.05%, respectively. The  
Figure 11: Classification results. (**a**) The proposed method, (**b**) M1, (**c**) M2, (**d**) M3, (**e**) Support Vector Machine, and (**f**) [PERSON]’s method. classification accuracy of lithology, especially for classes with high similarity, has been improved with the proposed method. From the results of M1, M2, and M3, we can conclude that dual-frequency Pol-SAR data can provide more information than single frequency Pol-SAR data; the accuracy of M3 is better than M1 and M2.