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In the previous years, Vision Transformer has demonstrated a global information extraction capability in the field of Computer Vision that CNN lacks. Due to the lack of inductive bias in Vision Transformer, it requires a large amount of data to support its training. In the field of remote sensing, it costs a lot to obtain a significant number of high-resolution remote sensing images. Most existing change detection networks based on deep learning rely heavily on CNN, which cannot effectively utilize the long-distance dependency between pixels for difference discrimination. Therefore, this work aims to use highperformance Vision Transformer to conduct change detection research with limited data. A Bi-branch Fusion Network Based on Axial Cross Attention (ACABFNet) is proposed. The network extracts local and global information of images through the CNN Branch and Transformer Branch respectively, and then fuses local and global features by bidirectional fusion approach. In the upsampling stage, similar feature information and difference feature information of the two branches are explicitly generated by feature addition and feature subtraction. Considering that the self-attention mechanism is not efficient enough for global attention over small datasets, we propose the Axial Cross Attention. First, global attention along the height and width dimensions of images is performed respectively, and then Cross Attention is used to fuse the global feature information along two dimensions. Compared with the original self-attention, the structure is more GPU-friendly and efficient. Experiment results on three datasets reveal that ACABFNet outperforms existing change detection algorithms.

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