High-resolution remote sensing image segmentation is a mature application in many industrial-level image applications and it also has military and civil applications. The scene analysis needs to be automated as much as possible with high-resolution remote sensing images. This plays a significant role in environmental disaster monitoring, forestry industry, agricultural farming, urban planning, and road analysis. This study proposes a multi-level feature fusion network (MFNet) that can integrate the multi-level features in the backbone to obtain different types of image information. Finally, the experiments in this study demonstrate that the proposed network can achieve good segmentation results in the Vaihingen and Potsdam datasets. By aiming to achieve a large difference in the scale of the target objects in remote sensing images and achieving a poor recognition result for small objects, a multi-level feature fusion solution is proposed in this study. This investigation improves the recognition results of the remote sensing image segmentation to a certain extent.
Keywords: feature fusion; image semantic segmentation; remote sensing image; scale difference.