Bi-SRNet

Pytorch codes of 'Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images' [paper]

Data preparation:

1. 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

2. 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.