Pytorch codes of 'Looking Outside the Window: Wider Context Transformer for the Semantic Segmentation of High-Resolution Remote Sensing Images' [paper]
BLU dataset [download link] [Baidu Netdisk]
To be updated:
- Codes for the BLU dataset
- Codes for the GID
- Codes for the Potsdam dataset
- Optimizing the codes to easily switch datasets
How to Use
- Split the data into training, validation and test set and organize them as follows:
YOUR_DATA_DIR
- Train
- image
- label
- Val
- image
- label
- Test
- image
- label
-
Change the training parameters in Train_WiCo_BLU.py, especially the data directory.
-
To evaluate, change also the parameters in Eval_WiCo_BLU.py, especially the data directory and the checkpoint path.
If you find our work useful or interesting, please consider to cite:
L. Ding et al., "Looking Outside the Window: Wide-Context Transformer for the Semantic Segmentation of High-Resolution Remote Sensing Images," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2022.3168697.