Remote Sensing Image Segmentation Network That Integrates Global–Local Multi-Scale Information with Deep and Shallow Features

As the spatial resolution of remote sensing images continues to increase, the complexity of the information they carry also grows. Remote sensing images are characterized by large imaging areas, scattered distributions of similar objects, intricate boundary shapes, and a high density of small objects, all of which pose significant challenges for semantic segmentation tasks. To address these challenges, we propose a Remote Sensing Image Segmentation Network that Integrates Global–Local Multi-Scale Information with Deep and Shallow Features (GLDSFNet). To better handle the wide variations in object sizes and complex boundary shapes, we design a Global–Local Multi-Scale Feature Fusion Module (GLMFM) that enhances segmentation performance by fully leveraging multi-scale information and global context. Additionally, to improve the segmentation of small objects, we propose a Shallow–Deep Feature Fusion Module (SDFFM), which effectively integrates deep semantic information with shallow spatial features through mutual guidance, retaining the advantages of both. Extensive ablation and comparative experiments conducted on two public remote sensing datasets, ISPRS Vaihingen and Potsdam, demonstrate that our proposed GLDSFNet outperforms state-of-the-art methods.