# Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images
## 4 Discussion  
With the help of polarimetric information, oil slicks and their biogenic films can be well separated. Experiments proved that the classification performance does not always increase with introducing more features; it fluctuates or decreases after the sufficient features are considered. This effect can be attributed to correlated and contradicting information carried in these features. In the demonstrated case, a set of four key features is sufficient, and the classification performance does not increase much when introducing more features. This phenomenon shows that most polarimetric information can be provided by several powerful and complementary features. As a result, in real applications, only a few \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{3}{c}{**Ground Truth (Pixels)**} \\ \cline{2-4}  & **Oil** & **Sea** & **Total** \\ \hline
**Oil** & 4879 & 197 & 5076 \\
**Sea** & 671 & 5338 & 6009 \\
**Total** & 5550 & 5535 & 11,085 \\ \hline \hline \end{tabular} Overall accuracy = 92.1696\% (10,217/11,085), kappa coefficient = 0.8434.  
\end{table} 
Table 13: Confusion matrix achieved by SVM based on four features derived from local linear embedding (LLE) on quad-pol SAR features. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{3}{c}{**Ground Truth (Pixels)**} \\ \cline{2-4}  & **Oil** & **Sea** & **Total** \\ \hline
**Oil** & 4649 & 82 & 4731 \\
**Sea** & 901 & 5453 & 6354 \\
**Total** & 5550 & 5535 & 11,085 \\ \hline \hline \end{tabular} Overall accuracy = 91.1322\% (10,102/11,085), kappa coefficient = 0.8227.  
\end{table} 
Table 12: Confusion matrix achieved by SVM based on four features derived from PCA on quad-pol SAR features. Figure 8: Classification results using SVM based on feature dimension reduction methods. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{3}{c}{**Ground Truth (Pixels)**} \\ \cline{2-4}  & **Oil** & **Sea** & **Total** \\ \hline
**Oil** & 4649 & 82 & 4731 \\
**Sea** & 901 & 5453 & 6354 \\
**Total** & 5550 & 5535 & 11,085 \\ \hline \hline \end{tabular} Overall accuracy = 91.1322\% (10,102/11,085), kappa coefficient = 0.8227.  
\end{table} 
Table 13: Confusion matrix achieved by SVM based on four features derived from local linear embedding (LLE) on quad-pol SAR features. representative features need to be extracted to save computing time and avoid the problem of \"curse of dimensionality\". In this study, we present a comparative study on features extracted from different polarimetric SAR modes to provide valuable information for oil spill classification. It was proven that quad-pol features have the highest overall accuracy, while \(\pi/2\) compact polarimetric SAR modes had the best performance among all compact and dual-polarimetric SAR modes, followed by \(HH/VV\) dual-polarimetric SAR modes. The lowest performance was achieved by \(\pi/4\) mode. In \(\pi/2\) mode, the circularly-polarized signal is transmitted, which has been proven to be more suitable for a series of marine remote sensing applications [6; 23], since it is very sensitive to the change of scattering mechanisms on the sea surface. \(HH\)-\(VV\) phase correlation is very helpful for distinguishing marine oil spill and biogenic oil slicks [3], and thus, \(HH/VV\) dual-polarization mode achieved relatively good performance. In fully and \(\pi/2\) compact polarimetric modes when the separability of the features is high, SVM achieved the highest performance in comparison with other supervised classifiers. The advantage of SVM is its good capability of handling the problem of the \"curse of dimensionality\". It has better performance in dealing with data of a high dimensional feature space in supervised classification applications, such as this illustrated case. For quad-pol feature-based classification, ANN performed slightly better than ML, and for other modes, ML performed better than ANN.