Semantic fusion block: enhanced transformer architecture for imbalanced remote sensing image semantic segmentation

In this paper, we explore strategies to address the issue of class imbalance in the domain of semantic segmentation of remote sensing images. Addressing the limitations of traditional CNNs when processing class-imbalanced datasets, we propose a novel feature extraction architecture based on Transformer. This architecture not only compensates for the deficiencies of Transformer in processing high-resolution remote sensing images but also enhances the model's performance on imbalanced datasets. Our method achieved a significant performance improvement in high-resolution image segmentation tasks with class imbalance, realizing a 68.35% mIoU, a notable advancement over existing baseline methods. The primary contribution of this study lies in providing an effective approach to handle the class imbalance problem in semantic segmentation of remote sensing images, paving a new path for research and practical applications in this field.