# Improved Sea Ice Image Segmentation Using U\({}^{2}\)-Net and Dataset Augmentation
## 2 Materials and Methods
### U\({}^{2}\)-Net Retrofit  
#### 2.2.1 U\({}^{2}\)-Net Network and Convolutional Block Attention Module (CBAM)  
Semantic segmentation networks have focused on extracting deep features using existing backbones, such as Alexnet, VGG, ResNet, ResNeXt, DenseNet, etc. The above networks have the following problems: first, the network backbones are designed for image classification tasks, and the features they extract represent semantic rather than local details and global contrast information, which are crucial for semantic segmentation; second, these networks need to be pretrained on the ImageNet dataset; third, these networks add feature aggregation modules in order to extract multilayer salient features leading to complex network structure; and fourth, the deeper architecture is achieved by sacrificing the high-resolution feature mapping. The U\({}^{2}\)-Net network has the following advantages: firstly, the network is a two-layer, nested, U-shaped structure that does not use a pretrained backbone model for image classification, and the model weights are trained from the training set; secondly, the new architecture allows the network to go deeper and obtain high-resolution features without substantially increasing memory and computational costs. In the bottom layer, a new RSU is designed to extract intrastage, multiscale features without reducing the feature mapping resolution. In the top layer, there is a U-Net-like structure, where each stage is populated by a residual U-block. The feature map is downsampled twice after each Encoder and upsampled before passing through each Decoder twice before passing through each decoder. The residual U-blocks in the network can be divided into two categories: Encoder1-Encoder4 and Decoder1-Decoder4. They use the same modular structure of RSU-L, only the depth (L) is different. Taking L = 7 as an example, the maximum downsampling time was 32 times. When Encoder5-6 and Decoder6 used the module RSU-F, the main difference between the RSU-L and RSU-F structures was that there was no more downsampling (purple part). Because the image size has been sufficiently downsampled, much contextual information will be lost when the image size is decreased again, which affects image segmentation. Therefore, RSU-4F is used in the deep layer, and the module decreases the downsampling part and uses multilayer expansion convolution to enlarge the receptive field. The right side shows the output of the fusion of the features of each layer after upsampling to obtain the segmented image as large as the original image, and the feature map of each layer is then convolved by channel number 1 to obtain the final output [24]. The CBAM contains the channel attention mechanism (channel attention module, CAM) and spatial attention mechanism (spatial attention module, SAM), with two sub-modules (Figures 1 and 2, respectively). The channel attention mechanism is a one-dimensional vector obtained by compressing a feature map in the spatial dimension. Mean and maximum pooling operations are used to compress the spatial and channel