Multi-Head Transposed Attention Transformer for Sea Ice Segmentation in Sar Imagery

Sea ice plays a pivotal role in the Earth’s climate system and exhibits high sensitivity to shifts in temperature and atmospheric conditions. The precise and timely assessment of sea ice parameters is essential for comprehending and forecasting the climate changes. However, the vast volume of satellite data covering ice-covered regions is impractical to be subjectively assessed. Hence, the utilization of automated algorithms becomes mandatory to fully exploit the continuous data streams from satellites. In this paper, we propose a UNet transformer-based architecture, called UT-MHTA, to sea ice segmentation using SAR satellite imagery. Our UT-MHTA network replaces the conventional multi-head attention (MHA) block with a multi-head transposed attention (MHTA) which can capture long-range pixel interactions, while still remaining suitable for large images. Our method demonstrates superior performance compared to state-of-the-art methods, without drastically raising the computational complexity. In particular, UT-MHTA achieves a mean intersection over union (mIoU) of 68.76% on the AI4Arctic data set, with an inference time of 865ms for a 400 km
²
product.