# Dark Spot Detection in SAR Images of Oil Spill Using Segnet
## 5 Conclusions and Outlooks  
The current research used Segnet to extract dark spots in SAR images of an oil spill. To reduce the computational and storage pressure of GPU, we chose a Segnet's batch size of 1 (i.e., inputting one sample at a time), and found that in this case, the Segnet achieved a better segmentation effect without using the batch normalization layer. The proposed method effectively distinguished between oil slicks and seawater based on the data set (OIL_SPILL_DATASET), and high accuracy segmentation results were obtained for SAR images with high noise and weak boundaries.  
The OIL_SPILL_DATASET was also applied to FCN8s and some other classical segmentation methods. By comparing the four parameters (PA, MA, MIoU, and FWIoU) of different addition and multiplication noise levels, the following trends were found:  
* Segnet and FCN8s showed high stability and tolerance to addition and multiplicative noise, although the overall performance of FCN8s was not as good as that of Segnet. In addition, Segnet was obviously superior to FCN8s in weak boundary regions.
* Some classical segmentation methods (such as SVM, CART, RF, and Otsu) were much more sensitive to addition and multiplicative noise than the deep learning models.  
However, Segnet's training process was supervised, and its training relies on a large number of label images. The production of labels was not only time-consuming and laborious in the data preparation stage, but also the training effect could be easily affected by human factors. In the future, we hope to shift to a weak or unsupervised training process to improve the convenience of application.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Training Set**} & \multicolumn{2}{c}{**Average PA**} & \multicolumn{2}{c}{**Average MA**} & \multicolumn{2}{c}{**Average MIoU**} & \multicolumn{2}{c}{**Average FWIoU**} \\ \cline{2-7}  & **Segnet** & **FCN8s** & **Segnet** & **FCN8s** & **Segnet** & **FCN8s** & **Segnet** & **FCN8s** \\ \hline No.1–No.3 & 0.9845 & 0.9589 & 0.9437 & 0.8644 & 0.9174 & 0.8419 & 0.9724 & 0.9481 \\ No.1 & 0.9858 & 0.9692 & 0.953 & 0.8952 & 0.9208 & 0.8637 & 0.9734 & 0.9547 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation of Segnet and FCN8s based on the four image segmentation standard values.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**K** & **Average** & **Variance** \\ \hline
3 & 0.966617469 & 0.010223167 \\
5 & 0.975305136 & 0.001727448 \\
7 & 0.985242757 & 0.000513106 \\
9 & 0.986190631 & 0.000499049 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Mean and variance of pixel-classification accuracy (PA) based on K-CV.  
**Author Contributions:** [PERSON] conceived and designed the algorithm, and constructed the outline for the manuscript; [PERSON] performed the experiments, analyzed the data, and made the first draft of the manuscript; [PERSON] was responsible for determining the overall experimental plan, and polished the manuscript.  
**Funding:** The work was carried out with the supports of the National Natural Science Foundation of China (Grant 61471079) and State Oceanic Administration of China for ocean nonprofit industry research special funds (No.2013418025).  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.