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This study develops a deep learning (DL) model to classify the sea ice and open water from synthetic aperture radar (SAR) images. We use the U-Net, a well-known fully convolutional network (FCN) for pixel-level segmentation, as the model backbone. We employ a DL-based feature extracting model, ResNet-34, as the encoder of the U-Net. To achieve high accuracy classifications, we integrate the dual-attention mechanism into the original U-Net to improve the feature representations, forming a dual-attention U-Net model (DAU-Net). The SAR images are obtained from Sentinel-1A. The dual-polarized information and the incident angle of SAR images are model inputs. We used 15 dual-polarized images acquired near the Bering Sea to train the model and employ the other three images to test the model. Experiments show that the DAU-Net could achieve pixel-level classification; the dual-attention mechanism can improve the classification accuracy. Compared with the original U-Net, DAU-Net improves the intersection over union (IoU) by 7.48.&#x0025; points, 0.96.&#x0025; points, and 0.83.&#x0025; points on three test images. Compared with the recently published model DenseNet<sub>FCN</sub>, the three improvement IoU values of DAU-Net are 3.04.&#x0025; points, 2.53.&#x0025; points, and 2.26.&#x0025; points, respectively.

Development of a Dual-Attention U-Net Model for Sea Ice and Open Water Classification on SAR Images