PSR-Net: A Dual-Branch Pyramid Semantic Reasoning Network for Segmentation of Remote Sensing Images

The long-range context information in the semantic segmentation network for remote sensing images (RSIs) plays an important role in the improvement of segmentation performance. However, in large RSIs, the interaction between local information and global information is limited. In order to solve the problem, we propose a dual-branch pyramid semantic reasoning segmentation network. Our dual-branch network consists of a global and local branch. The traditional CNN network is employed on the global branch, and a lightweight multi-scale hierarchical feature aggregation (MHFA) module is introduced into the local branch. In addition, the Feature Semantic Reasoning (FSR) module is proposed to enhance the valuable features and weaken the useless features to improve the semantic representation of RSIs, and then the double branch transformer is embedded. The ablation experiment on the Beijing Land-Use (BLU) dataset illustrates the effectiveness of the added modules, and the results presented by comparison with other traditional networks also confirm the superiority of our proposed network. The proposed network can achieve better segmentation accuracy on large-scale RSI datasets.