DS-SwinUNet: Redesigning Skip Connection With Double Scale Attention for Land Cover Semantic Segmentation
Year: 2024
Type: article
Abstract: In recent years, the development of visual transformer has gradually replaced convolutional neural networks in the visual domain with attention computation, causing pure transformer networks to become a trend. Despite significant advancements in semantic segmentation models for remote sensing, a critical gap remains in effectively capturing both local and global contextual information. Existing models often excel in either fine-grained local detail or long-range dependencies, but not both. Our work addresses this research gap by proposing the DS-SwinUNet model integrating convolutional operations with transformer-based attention mechanisms through the novel DS-transformer block, which consists of a two-scale attention mechanism incorporating convolutional computation and a modified FFN, and the module is placed in the skip connection section with Swin-UNet as the backbone. Experiments demonstrate that the transformer module proposed in this article improves the mIoU by 2.73% and 0.41% over the original Swin-UNet when the WHDLD and OpenEarthMap dataset are used as the segmentation task.
Authors: Zirui Shen, Wanjie Liu, Sheng Xu
Institution: Nanjing Forestry University
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,496
OpenAlex ID: W4404238623