Papers › A Novel Transformer Based Semantic Segmentation Scheme for Fine-Resolution Remote...
A Novel Transformer Based Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images
Libo Wang, Rui Li, Chenxi Duan, Ce Zhang, Xiaoliang Meng, Shenghui Fang
The fully convolutional network (FCN) with an encoder-decoder architecture has been the standard paradigm for semantic segmentation. The encoder-decoder architecture utilizes an encoder to capture multilevel feature maps, which are incorporated into the final prediction by a decoder. As the context is crucial for precise segmentation, tremendous effort has been made to extract such information in an intelligent fashion, including employing dilated/atrous convolutions or inserting attention modules. However, these endeavors are all based on the FCN architecture with ResNet or other backbones, which cannot fully exploit the context from the theoretical concept. By contrast, we introduce the Swin Transformer as the backbone to extract the context information and design a novel decoder of densely connected feature aggregation module (DCFAM) to restore the resolution and produce the segmentation map. The experimental results on two remotely sensed semantic segmentation datasets demonstrate the effectiveness of the proposed scheme.Code is available at https://github.com/WangLibo1995/GeoSeg
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semantic Segmentation | ISPRS Potsdam | DC-Swin | Mean F1 | 93.25 | #4 of 20 | Archive leaderboard | report |
| Semantic Segmentation | ISPRS Potsdam | DC-Swin | Mean IoU | 87.56 | #4 of 20 | Archive leaderboard | report |
| Semantic Segmentation | ISPRS Potsdam | DC-Swin | Overall Accuracy | 92.0 | #4 of 20 | Archive leaderboard | report |
| Semantic Segmentation | ISPRS Vaihingen | DC-Swin | Average F1 | 90.7 | #5 of 12 | Archive leaderboard | report |
| Semantic Segmentation | ISPRS Vaihingen | DC-Swin | Overall Accuracy | 91.6 | #5 of 12 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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