# Improved Sea Ice Image Segmentation Using U\({}^{2}\)-Net and Dataset Augmentation
###### Abstract  
Sea ice extraction and segmentation of remote sensing images is the basis for sea ice monitoring. Traditional image segmentation methods rely on manual sampling and require complex feature extraction. Deep-learning-based semantic segmentation methods have the advantages of high efficiency, intelligence, and automation. Sea ice segmentation using deep learning methods faces the following problems: in terms of datasets, the high cost of sea ice image label production leads to fewer datasets for sea ice segmentation; in terms of image quality, remote sensing image noise and severe weather conditions affect image quality, which affects the accuracy of sea ice extraction. To address the quantity and quality of the dataset, this study used multiple data augmentation methods for data expansion. To improve the semantic segmentation accuracy, the SC-U\({}^{2}\)-Net network was constructed using multi-scale inflation convolution and a multilayer convolutional block attention module (CBAM) attention mechanism for the U\({}^{2}\)-Net network. The experiments showed that (1) data augmentation solved the problem of an insufficient number of training samples to a certain extent and improved the accuracy of image segmentation; (2) this study designed a multilevel Gaussian noise data augmentation scheme to improve the network's ability to resist noise interference and achieve a more accurate segmentation of images with different degrees of noise pollution; (3) the inclusion of a multiscale inflation perceptron and multilayer CBAM attention mechanism improved the ability of U\({}^{2}\)-Net network feature extraction and enhanced the model accuracy and generalization ability. sea ice segmentation; U\({}^{2}\)-Net; remote sensing images +
Footnote †: journal: Medical  
multiple thresholding and random forest methods to invert FY-4A Bohai Sea regional images and introduced a seed-filling algorithm to revise the results, which improved the accuracy of sea ice inversion under non-clear sky conditions [4]. [PERSON] proposed turbid seawater end elements for Bohai Sea ice and water classification, used multifeature binomial tree classification to solve the problem of the difficult distinction between turbid seawater and sea ice, and used mixed-image element decomposition to enter the pixel interior to analyze its category [5]. [PERSON] et al. proposed an AL-TSVM sea ice image classification method from the perspective of combining active learning with semisupervised learning by combining a small amount of labeled, known training data and the overall data feature and spatial distribution patterns [6]. [PERSON] et al. obtained a combination of informative and low-similarity superior bands using the mutual information similarity metric and classified them using a support vector machine [7]. [PERSON] et al. improved the OSSP algorithm in three aspects: training set composition, classification result output, and tilted image geometry correction, to improve the classification accuracy of ship-based images [8]. [PERSON] proposed a pixel-level domain relationship context classification method for sea ice spatial neighborhood relations [9]. In the field of deep learning, [PERSON] et al. evaluated SegNet and PSPNet101 neural networks based on self-built training and testing sets. The sea ice classification dataset consisted of 1090 images with labels; for the test set of 104 images, the classification accuracy was 98.3% or better for both, validating the applicability of deep learning methods for sea ice detection [10]. [PERSON] et al. proposed a multilevel, feature-fusion image classification method based on a residual network PCA method to extract the first principal component of the original image, used a residual network to deepen the number of network- layer FPN, PAN, and SPP modules to increase the mining between layer and layer features, merged the features between different layers, and used the hyperspectral image of Bohai Bay for validation; the method improved the sea ice classification accuracy [11].