Pytorch codes of 'Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images' [paper]
Data preparation:
- Split the SCD data into training, validation and testing (if available) set and organize them as follows:
YOUR_DATA_DIR
- Train
- im1
- im2
- label1
- label2
- Val
- im1
- im2
- label1
- label2
- Test
- im1
- im2
- label1
- label2
- Find -datasets -RS_ST.py, set the data root in Line 22 as YOUR_DATA_DIR
Reference
If you find our work useful or interesting, please consider to cite:
Ding L, Guo H, Liu S, et al. Bi-temporal semantic reasoning for the semantic change detection in hr remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022.