# Multifeature Fusion Neural Network for Oceanic Phenomena Detection in SAR Images
## 4 Experiment and Analysis  
### Experiments on the Single Type of Oceanic Phenomena in SAR Images  
We provided a detailed introduction to the MFNN in Section 2 and then verified the validity of the network by analyzing actual images. We used the oceanic phenomena dataset to train the MFNN for 40,000 iterations. Figure 6 shows that the loss value of the MFNN no longer changes at approximately 35,000 iterations. Therefore, we used the network obtained through the 32,000 th training for testing.  
The MFNN was tested using images from the testing dataset. The correct detection results of the oceanic phenomena in the SAR slice images are shown in Figure 7, in which the green represents oceanic eddies, blue represents rain cells, purple represents ship wakes, red represents oceanic fronts, and yellow represents oil spills. Due to the weak boundaries of oceanic phenomena, the training image dataset only annotated the core region of the oceanic phenomena in the images. Therefore, testing was also based on whether an oceanic phenomenon core region was detected to determine the accuracy of the detection results.  
Figure 5: Examples of oceanic phenomena with their corresponding annotation.  
Figure 6: Loss value changes with the steps of iteration.  
We perform statistical analysis and accuracy calculations on the detection results of various oceanic phenomena. As Table 2 shows, the average accuracy of the test results reached 91%.  
In the research of some existing remote sensing image detection usages, DeepLabV3+ has shown excellent performance and better detection accuracy compared to other networks such as PspNet, SegNet, U-net, FCN, and so on [42; 43; 44; 45; 46; 47; 48]. To demonstrate the superior performance of MFNN, we conducted experiments using MFNN and DeepLabV3+, respectiv  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Phenomenon** & **Training Quantity** & **Testing Quantity** & **Correction Quantity** & **Accuracy** \\ \hline Oceanic eddy & 300 & 100 & 97 & 97\% \\ Rain cell & 300 & 100 & 95 & 95\% \\ Oceanic front & 300 & 100 & 91 & 91\% \\ Ship wake & 300 & 100 & 85 & 85\% \\ Oil spill & 300 & 100 & 87 & 87\% \\ Total & 1500 & 500 & 455 & 91\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of oceanic phenomena detection results.  
Figure 7: Oceanic phenomena detection results. (**a**) Examples of oceanic eddy detection results. (**b**) Examples of rain cell detection results. (**c**) Examples of ship wake detection results. (**d**) Examples of oceanic front detection results. (**e**) Examples of oil spill detection results.  
classifications of each kind of phenomenon detected by DeepLabV3+ and the MFNN is counted, and a histogram is plotted in Figure 8 to compare the results.  
According to the detection results of DeepLabV3+ and MFNN, the corresponding confusion matrix is shown in Figure 9.  
It can be seen that the MFNN has better performance in the detection, and the detection effect on linear targets has been significantly improved. From Table 3, the results show that MFNN is the winner compared with DeepLabV3+ on any metrics (precision, recall, F1, and accuracy).