Improved U-Net Multi-scale Feature Fusion Semantic Segmentation Network for RemoteSensing Images

High spatial resolution of remote sensing images,the large scale differences of different types of objects,and the imba-lance of categories are the main challenges faced by accurate semantic segmentation tasks.In order to improve the accuracy of semantic segmentation of remote sensing images,this paper proposes an improved U-Net multi-scale feature fusion semantic segmentation network for remote sensing image(Multi-scale Feature Fusion Network,MFFNet).The network is based on the U-Net network and includes a dynamic feature fusion module and a gated attention convolution mix module.Among them,the dynamic feature fusion module replaces the skip connection and improves the feature fusion method of the upsampling layer and the downsampling layer to avoid information loss caused by feature fusion,while improving the fusion effect of shallow features and deep features.Gated attention convolution mix module integrates self-attention,convolution,and gating mechanisms to better capture both local and global information.Comparative experiments and ablation experiments are carried out on Potsdam and Vaihingen.The results show that the mIoU of MFFNet on the two datasets reached 76.95% and 72.93% respectively,effectively improving the semantic segmentation accuracy of remote sensing images.