# Improved Sea Ice Image Segmentation Using U\({}^{2}\)-Net and Dataset Augmentation
## 4 Discussion  
### Data Augmentation Experiments  
U\({}^{2}\)-Net (Training set 1) is very sensitive to noise, and the low intensity of Gaussian noise makes it difficult for Model 1 to achieve the semantic segmentation of sea ice images. When the test set did not contain noise, the recall was mainly concentrated at 0.8-1.0, and when the noise level was 0.05, the recall was bimodal (partly concentrated in 0.8-1.0 and partly concentrated in 0-0.2, which indicates that the weak noise interfered with the prediction of the model and can only achieve more accurate segmentation for a part of the images, while the segmentation accuracy of another part of the images was close to 0; the noise level was 0.1, and the recall of all predicted images was concentrated around 0. This shows that noise reduces the accuracy of U\({}^{2}\)-Net semantic segmentation. There are two main reasons why U2-Net (Training set 1) is sensitive to noise. On the one hand, it is the convolutional neural network itself, and there is an overfitting phenomenon in the network training; on the other hand, the training dataset is small, and the single feature of the sea ice image makes it difficult for the network to recognize more complex ice conditions. To enhance the generalization ability of U\({}^{2}\)-Net, different levels of noise are added to the training set for augmentation. The training set after adding noise has a minimal loss of accuracy, but the generalization ability of the model is enhanced. The comparative analysis led to the following conclusions: first, for the noise-free dataset, U\({}^{2}\)-Net (Training set 1) had \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Models**} & \multicolumn{3}{c}{**U\({}^{2}\)-Net (Training set 1)**} & \multicolumn{3}{c}{**SC-U\({}^{2}\)-Net (Training Set 5)**} \\ \hline \multirow{2}{*}{**Test Set**} & \multirow{2}{*}{**Indicators**} & \multirow{2}{*}{**IoU**} & \multirow{2}{*}{**F1**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**IoU**} & \multirow{2}{*}{**F1**} & \multirow{2}{*}{**Recall**} \\ \cline{1-1} \cline{5-7}  & Original image & 0.812 & 0.865 & 0.857 & 0.847 & 0.907 & 0.911 \\ \(90^{\circ}\) rotation & 0.793 & 0.857 & 0.845 & 0.827 & 0.894 & 0.898 \\ \(180^{\circ}\) rotation & 0.786 & 0.855 & 0.840 & 0.817 & 0.887 & 0.886 \\ Blur & 0.807 & 0.866 & 0.853 & 0.838 & 0.900 & 0.901 \\ Brighten & 0.797 & 0.856 & 0.843 & 0.836 & 0.897 & 0.898 \\ Mean of all test sets & 0.799 & 0.860 & 0.848 & 0.832 & 0.896 & 0.897 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy of U\({}^{2}\)-Net (Training set 1) and SC-U\({}^{2}\)-Net (Training set 5) on the augmented test set. Figure 17: U\({}^{2}\)-Net (Training set 1) and SC-U\({}^{2}\)-Net (Training set 5) accuracy rating box line diagram. the best segmentation effect; second, in terms of noise resistance, U\({}^{2}\)-Net (Training set 3) was better than U\({}^{2}\)-Net (Training set 2), which was better than U\({}^{2}\)-Net (Training set 1). Especially in U\({}^{2}\)-Net (Training set 1) (no noise was added to the training set), the noise made the prediction accuracy decay rapidly. Third, in terms of generalization, the model with the noisy training set consisted the interference of noise; the richer the noise level was, the stronger its noise resistance was. U\({}^{2}\)-Net (Training set 2) could predict images with low noise, but the predicted noise limit was approximately 0.15; U\({}^{2}\)-Net (Training set 3) had a stronger generalization ability than model U\({}^{2}\)-Net (Training set 2) and was able to segment images with more severe noise pollution. U-Net (Training set 4) was obtained using Training set 4, and the accuracy evaluation indices of U\({}^{2}\)-Net (Training set 4) were higher than those of U\({}^{2}\)-Net (Training set 1) when predicting the noiseless test set.