# Dark Spot Detection in SAR Images of Oil Spill Using Segnet
## 3 Dark Spot Detection Using Segnet
### Image Segmentation of Oil Spill Using Segnet  
Segnet is an end-to-end training process. In this experiment, the training process of the Segnet was based on 3600 training samples, of which 1800 were original images, 900 were additive noise images, and 900 were multiplicative noise images. Due to limitations of the GPU capabilities, one sample at a time was input during the training process. Here, the epoch was set at 30, and the fixed learning rate was 0.01.  
The structure and parameters of Segnet are shown in Figure 4. The application of Segnet excluding the batch normalization layer is shown in Figure 5. The training performance diagram of Segnet is shown in Figure 6. In our study, the weight initialization process of the encoder and decoder was based on the research of [PERSON] et al. [25]. When the learning rate is 0.0001, the training loss would be relatively large. When the learning rate is 0.001, the training would be relatively stable soon, but it is not as good as the training performance when the learning rate is 0.01.  
Parts of of the test results of Segnet model based on the OIL_SPILL_DATASET are shown in Figure 7. Where a, b, c, d, and e are representative samples of five boundary statuses, respectively, and a brief description of the five boundary statuses is shown in Table 3. It can be seen from Figure 7 that Status-a (medium boundary complexity) and Status-c (ideal boundary) achieved the best segmentation results in the five boundary statuses, and Status-b (strong noise) and Status-d (complex boundary)  
Figure 4: The structure and parameters of Segnet.  
Figure 5: The application of Segnet without the batch normalization layer.  
were slightly inferior. For Status-e (weak boundary), the Segnet can still effectively segment dark spots in general, although some backgrounds were incorrectly segmented into dark spots.  
We used a trained model to test samples with a learning rate of 0.01. The segmentation results did not achieve the expected results (seeing Figure 8). For the samples without noise or with low multiplicative and additive noise in the testing set, the segmentation effect was good. However, almost all pixels with high additive noise were predicted as the background. 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),  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Status ID** & **a** & **b** & **c** & **d** & **e** \\ \hline \multirow{2}{*}{Status of boundary} & Medium boundary complexity, low noise & Strong noise & Ideal boundary complexity & Weak boundary strength \\ \hline Number of samples & 30 & 30 & 30 & 30 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Five boundary statuses of oil slicks.  
Figure 6: Training performance of Segnet based on different learning rates.  
Figure 7: The segment effect of some test samples with different boundary status.  
and found that in this case, the Segnet can achieve a better segmentation effect without using the batch normalization layer [26]. Finally, the learning rate was 0.01, and the batch normalization layer was removed based on the basic structure of Segnet.