Stair fusion network for remote sensing image semantic segmentation

Semantic segmentation of very high spatial resolution remote sensing images plays a vital role in many fields, such as land resource management, urban planning, and biosphere monitoring. Due to the scare variance between different types of regions, it is important to fully utilize multi-scale features. Moreover, with the complexity of some ground objects, global semantic information should be specially considered. As a consequence, in this paper, we propose a stair fusion network to further refine and fuse low-level and high-level features. In addition, we propose a global information enhancement module (GIEM) to extract global semantic information from the high-level features and reduce the length of delivery chain from them to the final results via a skip connection. Experimental results demonstrate the effectiveness of our model.