# Improved Sea Ice Image Segmentation Using U\({}^{2}\)-Net and Dataset Augmentation
###### Abstract
[PERSON] proposed using the PCANet network to select adaptive convolutional filter banks to mine sea ice depth features, adding hash binarization mapping and chunked histograms to enhance feature separation and reduce feature dimensionality. The author designed a two-branch, multisource, remote sensing, deep learning model for optical and SAR images to obtain good classification results with fewer training samples [12]. The improved SIS-U-Net network outperformed the classical U-Net network by adding a residual structure and void space pyramidal pooling structure to the U-Net network [13]. [PERSON] et al. used the convolutional neural network (CNN) model for image segmentation and selected the appropriate cost and activation functions according to the principle of migration learning. They examined the HJ-1A/B Bohai Sea ice images as the experimental data source labeled samples and achieved better experimental results [14]. [PERSON] et al. proposed a spectral-spatial joint feature concept for hyperspectral sea ice image classification and designed a three-dimensional (3D-CNN) model to extract the deep spectral-spatial features of sea ice by conducting sea ice classification experiments using two hyperspectral datasets, Baffin Bay and Bohai Bay, with experimental results based on a single-feature CNN algorithm [15]. [PERSON] et al. used the advantage of a CNN in deep feature extraction to design a deep learning network structure for SAR and optical images to achieve sea ice image classification by feature extraction and feature-level fusion of heterogeneous data; the effectiveness of the method was verified using two sets of heterogeneous satellite data in the Hudson Bay area [16]. [PERSON] et al. classified the Beaufort Sea and Severnaya Zemlya based on a Micro Sea Ice Residual Convolution Network (MSI-ResNet); the MSI-ResNet method performed better than the traditional support vector machine (SVM) classifier for identifying sea ice [17]. [PERSON] et al. proposed an automatic LFSI extraction method for the Laptev Sea in the eastern Arctic Ocean based on the conditional generative adversarial network Pix2 Pix and validated it experimentally using true color images from the Moderate Resolution Imaging Spectroradiometer (MODIS) [18]. In terms of data augmentation, [PERSON] et al. proposed a data augmentation method based on image gradients, which can freely choose the number of expansions and image sizes to effectively expand the dataset, and demonstrated through comparison experimentsthat the accuracy of the network model was improved after expanding the dataset by this method; the improvement was more obvious when the dataset was small, and the model accuracy could be improved by effectively reducing overfitting. The improvement is more obvious in the case of small datasets and can effectively reduce overfitting to improve the model accuracy [19]. [PERSON] et al. fused random probability resampling with adaptive scale equalization, added the fusion expansion algorithm to different target detection algorithms for experiments, and verified that the expansion algorithm can effectively reduce misdetection and false detection of small targets in road scenes [20]. By improving the generator and the discriminator of the Wasserstein-Generative Adversarial Network (W-GAN) and introducing reconstruction and perceptual style loss to enhance the ability of generating remote sensing images by ship, [PERSON] et al. used the remote sensing images generated by ship-WGAN to train the image recognition model, and the recognition accuracy was substantially improved by sample expansion of the generated samples to achieve the effect of data augmentation [21]. The recognition accuracy was substantially improved by expanding the generated samples, and data enhancement was achieved. Sea ice dataset labeling requires manual interpretation and mapping, and relies on ship-based and shore-based observations, which are costly.