Swin Transformer Embedding UNet for Remote Sensing Image Semantic Segmentation

Global context information is essential for the semantic segmentation of remote sensing (RS) images. However, most existing methods rely on a convolutional neural network (CNN), which is challenging to directly obtain the global context due to the locality of the convolution operation. Inspired by the Swin transformer with powerful global modeling capabilities, we propose a novel semantic segmentation framework for RS images called ST-U-shaped network (UNet), which embeds the Swin transformer into the classical CNN-based UNet. ST-UNet constitutes a novel dual encoder structure of the Swin transformer and CNN in parallel. First, we propose a spatial interaction module (SIM), which encodes spatial information in…

• NN
  National Natural Science Foundation of China

  Awards: 62172417, 41871260, 61806206
• NS
  Natural Science Foundation of Jiangsu Province

  Award: BK20201346
• ST
  Six Talent Peaks Project in Jiangsu Province

  Awards: 2015-DZXX-010, 2018-XYDXX-044