## 3 Experimental Results
### Results and Discussion
The confusion matrix of the cross-validation testing result is shown in Tables 7-10. The best classification results were achieved by SAE on the largest data set: 20,000 training and 4000 testing \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multicolumn{3}{c}{**Number of Data Samples (Training-Testing) Accuracy**} \\  & \multicolumn{3}{c}{**(Execution Time/Seconds)**} \\ \cline{2-4}  & **a. 20,000–4000** & **b. 4000–800** & **c. 800–160** \\ \hline SVM (support vector Machine) & 1.26\% (1.16) & 1.38\% (0.07) & 1.64\% (0.01) \\ NN (nerual network) & 1.16\% (6.52) & 1.68\% (1.33) & 2.13\% (0.34) \\ PCA (principal component analysis)-SVM & 1.20\% (0.9) & 1.33\% (0.06) & 1.58\% (0.01) \\ PCA-NN & 1.17\% (6.2) & 1.48\% (1.2) & 1.90\% (0.35) \\ SAE (stacked autoencoder) & 1.05\% (6.33) & 1.33\% (1.24) & 1.39\% (0.32) \\ DBN (deep believe network) & 1.16\% (5.63) & 1.42\% (1.18) & 1.53\% (0.28) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Testing error (1-accuracy) of classification achieved by different classifiers and on different sizes of data samples. Figure 6: Scatter plots of the main original features **(a)** and the features derived by principal component analysis (PCA), deep believe network (DBN) and stacked autoencoder (SAE) **(b–d)**. samples. On the 24,000 testing set, 251 pixels were wrongly classified. 101 pixels of these 251 pixels were false positive (commission errors) and 150 pixels were false negative (omission errors). Similar false positive was achieved by DBN, with slightly higher false negative rate. In the confusion matrix achieved by NN and SAE, it can be discovered that compared with deep learning algorithms, they achieved lower false negative and higher false positive rates. From the binary output that achieved by SAE (Figure 7), it can be observed that a few pixels in the area covered by the biogenic slick are classified as mineral oil. The possible reason of these \"misclassifications\" is the affection of signal noise on space-borne SAR data or the uniform distribution of the mineral oil and biogenic slicks. This misinterpretation can be further eliminated by a simple postprocessing step. Corrosion and swelling algorithms can be applied on the binary classification result to fix the small holes (missing alarm) in large oil-covered areas and isolated positive targets (false alarm) in the sea surface area. The receiver operating characteristics (ROC) of these classifiers in oil spill detection are approached based on 20,000 training, 4000 testing (Figure 8a,b) and 4000 training, 800 testing samples (Figure 8c,d) respectively. All the ROC curves are very close to the upper-left corner of the ROC map (Figure 8a,c). In the zoomed-in map (Figure 8b,d), some minor differences can be observed. Compared with other classifiers, SVM achieved a lower true positive rate under low false positive rate requirements, and higher true positive rate under high false positive rate requirements. And for NN the situation is just opposite. Deep neural networks, SAE and DBN achieved a modest true positive rate in the whole false positive rate range. \begin{table}
\begin{tabular}{c c c c} \hline
**Confusion Matrix of SVM** & **Mineral Oil** & **Non Mineral Oil** & **Total** \\ \hline Mineral oil (truth) & 11,893 & 107 & 12,000 \\ Non mineral oil (truth) & 201 & 11,799 & 12,000 \\ \hline Total & 11,948 & 12,052 & 24,000 \\ \hline \end{tabular}
\end{table} 
Table 10: SAE Confusion Matrix (20,000 training, 4000 testing, six-fold) derived by SVM. \begin{table}
\begin{tabular}{c c c c} \hline
**Confusion Matrix of SAE** & **Mineral Oil** & **Non Mineral Oil** & **Total** \\ \hline Mineral oil (truth) & 11,844 & 150 & 12,000 \\ Nonmineral oil (truth) & 101 & 11,896 & 12,000 \\ \hline Total & 11,948 & 12,052 & 24,000 \\ \hline \end{tabular}
\end{table} 
Table 7: Confusion Matrix (20,000 training, 4000 testing, six-fold) derived by SAE.