RELATING CNN-TRANSFORMER FUSION NETWORK FOR REMOTE SENSING CHANGE DETECTION

Network Architecture

The architecture of our proposed model is as follows

How to use

• Prepare the data:

  • Download datasets
  • LEVIR
  • BCDD
  • SYSU
  • Crop LEVIR and BCDD datasets into 256x256 patches.
  • Generate list file as ls -R ./label/* > test.txt
  • Prepare datasets into the following structure and set their path in train.py and test.py

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

• Prerequisites for Python:

  • Creating a virtual environment in the terminal: conda create -n RCTNet python=3.8
  • Installing necessary packages: pip install -r requirements.txt

• Evaluate pretrained models If you want to evaluate your trained model, you can run:

  • sh test.sh

• Train your model You can re-train our modelby using:

  • sh train.sh