Abstract: The emergence of convolutional neural network and Transformer model has made continuous progress in remote sensing image change detection technology， but at present， these two methods still have shortcomings. On the one hand， the convolutional neural network cannot model the global information of remote sensing images due to its local perception of convolution kernel. On the other hand， although Transformer can capture the global information of remote sensing images， it cannot model the details of image changes well， and its computational complexity increases quadrally with the resolution of images. In order to solve the above problems and obtain more robust change detection results， this paper proposes a CNN-Transformer Change Detection Network （CTCD-Net） based on convolutional neural network and Transformer hybrid structure. Firstly， CTCD-Net uses convolutional neural network and Transformer based on encoding and decoding structure in series to effectively encode local and global features of remote sensing images， so as to improve the feature learning ability of the network. Secondly， the cross-channel Transformer self-attention module （CSA） and attention feedforward network （A-FFN） are proposed to effectively reduce the computational complexity of Transformer. Full experiments on LEVIR-CD and CDD datasets show that the detection accuracy of CTCD-Net is significantly better than that of other mainstream methods.

Key words: remote sensing images, change detection, convolutional neural networks, Transformer