# Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images
## 3 Results
### Oil Spill Classification Based on Different Polarimetric SAR Modes  
In this part, as listed in Table 7, dual- and compact polarimetric SAR features are extracted from simulated SAR datasets (the conformity coefficient is only available in \(\pi/2\) mode). The overall classification accuracy of three classifiers based on the features extracted from different polarimetric SAR modes is compared in Figure 6. \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** & 5427 & 232 & 5659 \\
**Sea** & 123 & 5303 & 5426 \\
**Total** & 5550 & 5535 & 11,085 \\ \hline \hline \end{tabular} Overall accuracy = 96.7975\(\%\) (10,730/11,085), kappa coefficient = 0.9359.  
\end{table} 
Table 6: Confusion matrix achieved by ANN based on 9 fully polarimetric features. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Number**} & **Feature*** \\ \hline
1 & \(E_{V}^{2}\) \\
2 & Pedestal Height (CP) \\
3 & Entropy (CP) \\
4 & DoP (CP) \\
5 & Correlation Coefficient (CP) \\
6 & Alpha Angle (CP) \\
7 & Coherency Coefficient (CP) \\
8 & Ellipticity \(\chi\) (CP) \\
9 & CPD Standard Deviation (CP) \\
10 & Conformity Coefficient (\(\pi/2\)) \\ \hline \hline \end{tabular}  
* Features 1–9 are extracted from dual and compact polarimetric SAR data following the methods introduced in Section 2.4, while Feature 10 is only available for \(\pi/2\) mode. “CP” stands for features derived from compact polarimetric SAR data in order to distinguish them from those calculated from quad-pol SAR data.  
\end{table} 
Table 7: Uniform dual and compact polarimetric features considered in the study. \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** & 5429 & 178 & 5607 \\
**Sea** & 121 & 5357 & 5478 \\
**Total** & 5550 & 5535 & 11,085 \\ \hline \hline \end{tabular} Overall accuracy = 97.3027\(\%\) (10,786/11,085), kappa coefficient = 0.9461.  
\end{table} 
Table 4: Confusion matrix achieved by SVM based on 8 fully polarimetric 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** & 5411 & 256 & 5667 \\
**Sea** & 139 & 5279 & 5418 \\
**Total** & 5550 & 5535 & 11,085 \\ \hline \hline \end{tabular} Overall accuracy = 96.4366\(\%\) (10,690/11,085), kappa coefficient = 0.9287.  
\end{table} 
Table 5: Confusion matrix achieved by ML based on 4 fully polarimetric features. Quad-pol (QP) feature-based classification has the highest OA, followed by \(\pi/2\) compact polarimetric SAR mode and \(HH/VV\) dual-polarized (DP) mode. \(\pi/4\) mode-based classification has the lowest performance. In QP and \(\pi/2\) modes, SVM achieved the best performance, while for \(HH/VV\) DP and \(\pi/4\) modes, better performance was achieved by ML. Furthermore in dual- and compact polarimetric SAR modes, ML outperformed ANN; this may be explained by the fact that ANN has a higher requirement to the separability of the dataset and is more vulnerable to the loss or mixture of crucial information of the dataset. The confusion matrices of the classification results achieved by SVM based on features extracted from different polarimetric SAR modes are listed in Tables 8-10, with the feature number that achieved the best classification performance, and the classification results are demonstrated in Figure 7a-c. Similar supervised classification experiments were also conducted based on single polarimetric feature \(S_{VV}^{2}\) only. A much lower overall accuracy (61.7772%) and kappa coefficient (0.2348) were obtained. Figure 7d shows the classification result, from which it could be observed that most parts of the biogenic slick were misclassified to mineral oil. The confusion matrix (Table 11) further supported this observation.