TGCNet: A Fusion Double-Branch Network with Transformer Guiding CNN for Semantic Segmentation of Remote Sensing Images

Such networks often use CNN and Transformer to obtain local details and global dependencies of the image, respectively. In order to further make the model pay more attention to the local features of remote sensing images, and solve the problems such as omission and wrong recognition caused by the difficulty of edge information extraction in the semantic segmentation task of remote sensing images, in this paper, a fusion double-branch network with Transformer giding CNN (TGCNet) is proposed.

The whole frame of the model adopts encoder-decoder structure. In the encoder, Transformer branch in addition to normal coding, the global information acquired by Transformer is stratifiedly fused with the local spatial details corresponding to each layer for feature fusion, and input to the next layer of CNN coding. In addition, there is a slight difference between the feature output of Transformer and that of CNN, and feature loss may occur when feature fusion is carried out between the two outputs.