# Improved Sea Ice Image Segmentation Using U\({}^{2}\)-Net and Dataset Augmentation
## 2 Materials and Methods
### Sea Ice Image Segmentation Experimental Setup  
In order to improve the accuracy of sea ice image segmentation, this paper improves two aspects of data augmentation and the U\({}^{2}\)-Net network structure. In terms of data augmentation, five datasets were constructed using various data augmentation methods (such as noisy data augmentation), the model weights were obtained by training the U\({}^{2}\)-Net network with the five datasets, respectively, and the accuracy was checked using the test set. In terms of network structure improvement, a multilayer CBAM attention mechanism and multiscale expanded convolution were added to U\({}^{2}\)-Net to enhance the network feature extraction ability, and the accuracy and generalization ability of the improved model were checked using the test set. The SC-U\({}^{2}\)-Net network model was constructed by adding a multilayer, CBAM attention mechanism and multiscale inflation convolution to enhance the network feature extraction ability. The accuracy and generalization ability of the improved model were tested using test data (Figure 6). Figure 5: Improving the U\({}^{2}\)-Net network. Figure 6: Flow chart of sea ice segmentation. The initial learning rate was set to 0.001, and this study used the learning rate warmup. The learning rate warmup [29] and cosine annealing [30] were used to combine the learning rate change strategy. The weights of the neural network were randomly initialized at the beginning of training, and the warmup gradually increased the learning rate from low to high to ensure a good convergence of the network. When the gradient descent algorithm was used to optimize the objective function, cosine annealing reduced the learning rate of the cosine function as it approached the global minimum of the loss value, making the model as close to the optimal value as possible. The loss function is a binary cross-entropy loss function (Equation (7)), where \(\hat{\underline{y}}\) is the result of the model prediction sample and \(y\) is the sample label. The loss function of U\({}^{2}\)-Net is calculated in Equation (8), which contains two parts: \(\sum\limits_{m=1}^{M}w_{side}^{(m)}l_{side}^{(m)}\) represents the sum of the cross-entropy of the output results of different depth decoder and GT images, and \(w_{fused}l_{fuse}\) is the cross-entropy loss of the final output and GT images after multichannel fusion. \[Loss=-(y\cdot\log(\hat{\underline{y}})+(1-y)\cdot\log(1-\hat{\underline{y}}) \tag{7}\]  
\[L=\sum\limits_{m=1}^{M}w_{side}^{(m)}l_{side}^{(m)}+w_{fused}l_{fuse} \tag{8}\]  
The validation accuracy of the model increased with an increase in epochs, and the model started to converge when the epoch reached 360 when the U\({}^{2}\)-Net network was trained using training set 1. To balance model accuracy and training efficiency, the epoch was set to 360 in this study. Five test sets were used for training, in which the epochs of training datasets 1 and 2 were set to 360, the epochs of datasets 3, 4, and 5 were set to 90, and the epochs of datasets 3, 4, and 5 were set to 90. The accuracy evaluation metrics selected in this study are the intersection over union (IoU, F1-score, and recall. If sea ice is called a positive case (positive), non-sea ice is called a negative case (negative), and the classifier predicting correctly is noted as true (true) and incorrectly predicting as false (false), and the four basic terms are combined with each other to form the four basic elements of the confusion matrix: true case (TP), false positive case (FP), false negative case (FN), and true negative case (TN). Then IoU, F1-score, and recall are calculated by Equations (9)-(11).