# Dark Spot Detection in SAR Images of Oil Spill Using Segnet
## 4 Analysis of Segmentation Experiments
### Stability Analysis  
Due to the influence of the sea surface environment (such as waves, ocean currents, and low wind belts) and the characteristics of SAR sensors, high noise and weak boundaries are commonly found in SAR images of oil spill. Figure 14 shows an example of the segmentation effect of a test sample at five additional noise levels. The first row is a SAR test sample with five different peak signal-to-noise ratio (PSNR), and the second row is the label of each sample. The outputs of Segnet and FCN8s are listed in the third and fourth rows, respectively. Figure 15 shows the segmentation effect of the same test sample at five multiplicative noise levels.  
Figure 13: Comparison of the four evaluation parameters with five boundary statuses.  
Figure 14: Comparison of Segnet and FCN8 with five levels of additive noise.  
Figure 15: Comparison of Segnet and FCN8 with five levels of multiplicative noise.  
Correspondingly, the effectiveness of the proposed method is demonstrated through the analysis of some experimental results. The same training set and test set were applied to FCN8s and some classical image segmentation methods, such as SVM, CART [27], RF [28], and Otsu. The four aforementioned evaluation parameters (PA, MA, MIoU, and FWIoU) at 10 additive noise levels are shown in Figure 16, and a comparison at 10 multiplicative noise levels is shown in Figure 17, where the X coordinate is PSNR. We can see the following trend:  
1. In addition to some fluctuations of MA of Segnet where PSNR is relatively large in Figure 16, the other three parameters (PA, MIoU, and FWIoU) are basically on a horizontal line, which proves that Segnet shows high robustness in terms of additive noise.
2. When PSNR is generally less than 35 in Figure 16, all four indicators of FCN8s have a clear downward trend, which indicates that FCN8S is not as stable as Segnet when the additive noise is relatively high.
3. In Figure 16, the four classical segmentation methods (SVM, CART, RF, and Otsu) are sensitive to additive noise (especially when PSNR is generally less than 35), and the comparison of the three indicators (MA, MIoU, and FWIoU) shows that they are not as good as Segnet and FCN8s. Although these three methods (SVM, CART, and RF) seem to have a similar performance with Segnet and FCN8s based on PA, this phenomenon should be related to PA's defects. It is very difficult to ensure that oil slicks and seawater have the same initial probability in the testing set.
4. In Figure 17, Segnet and FCN8s show high stability and tolerance to multiplicative noise, although the overall performance of FCN8s is not as good as that of Segnet. When PSNR is less than 35, the PA of FCN8s is obviously decreased.
5. The four classical segmentation methods (SVM, CART, RF, and Otsu) are much more sensitive to multiplicative noise than Segnet and FCN8s, especially when the noise is high, and the performance of those classification methods drops sharply. In addition, Otsu's performance is significantly worse than the other three methods.  
Figure 16: Four image segmentation standard values under 10 additive noise levels.  
Overall, by comparing the four parameters (PA, MA, MIoU, and FWIoU) of the additive and multiplicative noise in Figures 16 and 17, the traditional machine algorithm performed poorly in detecting dark spots compared with semantic segmentation algorithms. Table 5 shows the comparison of segmentation accuracy (averages values of PA, MA, MIoU, and FWIoU) and running time (GPU time) using the same test set (600 samples). Due to the complex structure of the deep learning model, its running time was much longer than that of the classical machine learning model.