## 1 Introduction
For example, [PERSON] [11] was the first to use deep learning convolutional neural networks (CNNs) to map sea ice concentrations in 2016 and illustrated the potential of deep learning in relation to the study of classification methods for two open ocean categories and four sea ice categories in response to the different representations of the features of different polar categories in Sentinel-1A images. [PERSON] [12] applied a fully convolutional network of U-Net networks to a Sentinel-1 SAR image classification prediction task. [PERSON] [13] applied CNN models in sea ice concentration assessments and sea ice classification experiments, resulting in an overall classification accuracy of 93% and a kappa coefficient of 0.8. Deep learning semantic segmentation methods can better tap into the deep non-linear relationships of image features and thus better extract the change patterns of data features, which can then be applied in the field of sea ice segmentation tasks. In summary, during the summer sea ice freezing and melting period, regional sea ice conditions are complex, and sea ice classification becomes a more and more difficult problem. Deep learning also has problems; for example, deep learning-based methods require a large number of labeled samples, which entails a lot of manual work, and the differences between sea ice categories are small, while their interpretation often requires the input of experienced professionals. Meanwhile, although SAR has the advantages of being all-weather and unaffected by cloudy images, the SAR data source itself has fewer feature dimensions compared with optical remote sensing and lacks rich feature information. In this paper, remote sensing sea ice image classification in the Arctic summer is addressed using dual-polarized Sentinel-1 SAR C-band images. Under the conditions related to melting sea ice, it is difficult to distinguish ice types in the C-band [14], meaning it will be important to figure out how to extract deeper feature information useful for sea ice classification using deep learning methods. This paper focuses on the optimization of the automatic summer sea ice classification process for SAR images and peri-ice map data. A high-resolution sea ice concentration feature extraction method was designed to improve sea ice classification based on SAR images of summer sea ice melt cycles. Compared with traditional sea ice concentration extraction methods, this method uses convolution operations and array computation operations to quickly and accurately complete regional high-resolution sea ice concentration feature extraction tasks. A deep learning fully convolutional neural network--the Double Fusion Module of a U-shaped High-Resolution Network (DF-UHRNet)--is also proposed. It is a semantic segmentation model combining U-Net and HRNet structures. The effective segmentation of summer sea ice was achievedby designing two fusion modules, introducing an attention mechanism, a conditional random field, and morphological post-processing modules. The specific contributions of our work are as follows:  
1. We propose a U-shaped network architecture using the two fusion modules. Compared with the U-HRNet and HRNet networks separately, it requires fewer network parameters and can more effectively fuse neighboring scale semantic features and extensively extract global contextual information to achieve summer sea ice classification. 2. We designed a sea ice concentration extraction method based on the K-means clustering algorithm and convolution operation, which can extract high-resolution information regarding the sea ice concentration in the Arctic region during summer using SAR images, is faster in terms of extraction, and can obtain a better resolution compared with traditional methods. 3. We propose a deep learning-based process of optimization for the automatic classification of Arctic summer sea ice using SAR and ice map data, enabling the one-click extraction and identification of sea ice in the region.