TransUNetCD: A Hybrid Transformer Network for Change Detection in Optical Remote-Sensing Images

In the change detection (CD) task, the UNet architecture has achieved superior results. However, due to the inherent limitation of convolution operations, UNet is inadequate in learning global context and long-range spatial relations. Transformers can capture long-range feature dependencies, but the lack of low-level details may result in limited localization capabilities. Therefore, this article proposes an end-to-end encoding–decoding hybrid transformer model for CD, TransUNetCD, which has the advantages of both transformers and UNet. The model encodes the tokenized image patches from the convolutional neural network (CNN) feature map to extract rich global context information. The decoder upsamples the…

• NN
  National Natural Science Foundation of China

  Award: 42071444