# Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data
## 4 Discussion  
The objective of this study is to classify lithology using dual-frequency Pol-SAR data. In this paper, new features are extracted from dual-frequency Pol-SAR data to increase the class separability of the input data, and the SSAE is selected to obtain deep features of the input data for lithology classification. From Figure 10, we can see that lithology is classified successfully with the proposed method. To verify the performance of the proposed method, several methods including M1, M2, M3, SVM, and Hou's method were applied to the same dataset for classification of lithology. The  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **The Proposed Method** & **M1** & **M2** & **M3** & **SVM** & **Hou’s Method** \\ \hline Time consumed (s) & 1871 & 1781 & 1812 & 1839 & 4977 & 1867 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Time consumed.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **The Proposed Method** & **M1** & **M2** & **M3** & **SVM** & **Hou’s Method** \\ \hline Class 1 & 0.9885 & 0.9154 & 0.9719 & 0.9863 & 0.9813 & 0.9885 \\ Class 2 & 0.9869 & 0.5497 & 0.5754 & 0.9450 & 0.7250 & 0.5475 \\ Class 3 & 0.9784 & 0.7628 & 0.9982 & 0.9439 & 0.9640 & 0.9578 \\ Class 4 & 0.9910 & 0.7912 & 0.7147 & 0.8716 & 0.8185 & 0.8772 \\ Class 5 & 0.9954 & 0.6190 & 0.8800 & 0.9121 & 0.7145 & 0.6922 \\ Class 6 & 0.9801 & 0.6843 & 0.8690 & 0.9743 & 0.9443 & 0.8744 \\ Class 7 & 0.9979 & 0.8295 & 0.7000 & 0.9514 & 0.9155 & 0.8227 \\ Class 8 & 0.9990 & 0.6772 & 0.8141 & 0.9446 & 0.8910 & 0.6680 \\ \hline OA & 0.9890 & 0.7299 & 0.8055 & 0.9470 & 0.8669 & 0.8005 \\ Kappa & 0.9873 & 0.6884 & 0.7742 & 0.9385 & 0.8458 & 0.7704 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification accuracy.  
accuracy of M1 and M2 is 72.99% and 80.55%. The accuracy of these methods is low because they only use single frequency Pol-SAR data. The performance of [PERSON]'s method is 80.80%. The classification performance of SVM is 86.69% and the SVM consumes more time than the other methods. Both M3 and the proposed method achieved more than 90% classification accuracy. Accuracy and an acceptable time consumed is essential in the classification with the proposed method. Therefore, the proposed method has the best result of lithology classification.  
Classification of lithology is important in geological exploration, thus the method proposed in this paper can be used widely in the future. If we can obtain a few labels spread over a specific region by manual investigation, we can propagate this knowledge over the entire region. Besides, we also can train the network on one set of a region and apply it to another region, if the data of these regions are acquired under the same radar imaging conditions.  
The current paper used SSAE to extract deep features of input data and classify lithology. In the future, new classifiers can be applied in the proposed method to improve the classification accuracy. Recently, a combination of results from different classifiers has been proposed [30] and our future study will investigate this approach, which may improve the results obtained in this paper.