Sea ice is a crucial component of the Earth's climate system and is highly sensitive to changes in temperature and atmospheric conditions. Accurate and timely measurement of sea ice parameters is important for understanding and predicting the impacts of climate change. Nevertheless, the amount of satellite data acquired over ice areas is huge, making the subjective measurements ineffective. Therefore, automated algorithms must be used in order to fully exploit the continuous data feeds coming from satellites. In this paper, we present a novel approach for sea ice segmentation based on SAR satellite imagery using hybrid convolutional transformer (ConvTr) networks. We show that our approach outperforms classical convolutional networks, while being considerably more efficient than pure transformer models. ConvTr obtained a mean intersection over union (mIoU) of 63.68% on the AI4Arctic data set, assuming an inference time of 120ms for a 400 x 400 squared km product.

翻译：海冰是地球气候系统的关键组成部分，对温度和大气条件变化高度敏感。准确及时地测量海冰参数对于理解和预测气候变化的影响至关重要。然而，冰区卫星数据量巨大，使得主观测量方法效果有限。因此，必须采用自动化算法来充分利用卫星持续传输的数据流。本文提出了一种基于SAR卫星影像的混合卷积变压器（ConvTr）网络进行海冰分割的新方法。研究表明，该方法性能优于传统卷积网络，且效率远高于纯Transformer模型。在AI4Arctic数据集上，ConvTr的平均交并比（mIoU）达到63.68%，对400×400平方公里产品的推理时间仅为120毫秒。