Requirements

• Python 3.6

• Pytorch 1.4

• torchvision 0.5.0

Dataset

• CDD (Change Detection Dataset)

• LEVIR,

• SYSU

• Crop LEVIR and BCDD datasets into 256x256 patches. The pre-processed BCDD dataset can be obtained from BCDD_256x256.

• Prepare datasets into the following structure and set their path in metadata.json

  ├─Train
      ├─A        ...jpg/png
      ├─B        ...jpg/png
      ├─label    ...jpg/png
      └─list     ...txt
  ├─Val
      ├─A
      ├─B
      ├─label
      └─list
  ├─Test
      ├─A
      ├─B
      ├─label
      └─list
  

Train from scratch

python train_val.py

Evaluate model performance

python eval.py

Visualization

python visualization.py

######################################## Using the code should cite the following paper

[1] X. Zhang,Shuli Cheng*, L. Wang and H. Li, Asymmetric Cross-attention Hierarchical Network Based on CNN and Transformer for Bitemporal Remote Sensing Images Change Detection,IEEE Transactions on Geoscience and Remote Sensing，2023,61：1-16 (https://github.com/cslxju/ChangeDetection_ACAHNet_TGRS2023)

https://ieeexplore.ieee.org/document/10045704

[2] X. Zhang, L. Wang and S. Cheng, "A Multiscale Cascaded Cross-Attention Hierarchical Network for Change Detection on Bitemporal Remote Sensing Images," in IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-16, 2024