# Dark Spot Detection in SAR Images of Oil Spill Using Segnet
## 3 Dark Spot Detection Using Segnet
### Comparison of Segnet to FCN  
Three end-to-end FCN models (i.e., FCN32s, FCN16s, and FCN8s) were proposed by [PERSON] et al. [14], among which FCN8s (8-step sampling) was considered the best one. The FCN8s encoder includes convolution layers with a \(3\times 3\) convolution kernel. The convolution layers changed from the last three full connection layers are convolution kernels of \(7\times 7\), \(1\times 1\), \(1\times 1\), and the convolution layers of layers 6-7 are all characteristic images \(1\times 1\times 4096\). The last de-convolution layer can be considered as an up-sampling process, which can be used to obtain the segmentation image with the same size as the original image. The up-sampling process of FCN8s is a jump architecture, which performs up-sampling on the results of different pool layers of pool 3, pool 4, and pool 5, and then optimizes the output according to these results. The size of the output picture was the same as that of the input, and its number of channels was 2, which indicates that the output prediction picture contained two categories (seawater and oil slicks). A schematic diagram of FCN8s' model operation is given in Figure 9.  
In our study, the FCN8s' structure did not include the batch normalization layer. We tested some learning rates (0.01, 0.001, 0.0001) during FCN8s' training and found that the required accuracy could be obtained when the learning rate was 0.001, but the cost was a longer training period than Segnet. The training parameters of FCN8s and Segnet are shown in Table 4. The training performance of FCN8s is shown in Figure 10. When epoch reached 10, the training loss was close to 0.06 and tended to be stable.  
Figure 8: Comparison of segmentation results with different levels of additive noise.  
Figure 9: The structure and parameters of FCN8s (8-step sampling).  
The comparison between Segnet and FCN8s is shown in Figure 11, and the five samples (a-e) represent the boundary statuses, respectively (see Table 3). The results show that FCN8s has a good overall segmentation effect. However, the performance needs to be improved in oil spill images with weak boundaries and high boundary complexity. The Sample-d and Sample-e are both high wind speed regions in Figure 11. Due to the high wind speed, the oil slick boundary complexity in Sample-d was high, and the segmentation results were not ideal.  
The receiver operating characteristic (ROC) analysis was used to evaluate the proposed algorithm with the pixel classification accuracy of FCN8s. Since our model input was a sample containing both oil slicks and seawater, it was difficult to ensure that the pixel ratio of oil slicks and seawater was 1:1. To ensure that the pixel ratio of oil slicks and seawater was as close as possible to 1:1, we re-selected the training set and the testing set. According to the label of each sample in the training set, the number of pixels of oil slicks and seawater in each sample was calculated, respectively, and finally, 2800 pieces of data with the pixel-to-pixel ratio of oil slicks and seawater of 0.998:1 were selected. The testing set selected 200 test data with a ratio of 0.989: 1 from 600 testing sets. The ratio of the training set to testing set was still 6:1. The ROC curves for Segnet and FCN8s are shown in Figure 12, and we can see that the ROC curves of Segnet and FCN8s are very close to the upper left corner, but there are still some differences. Under the condition of a high false positive rate (FPR), both showed a higher true positive rate (TPR). However, under the condition of low FPR, the TPR of FCN was lower than that of Segnet. The results show that Segnet has achieved a moderate TPR in the whole range of FPR.