LGCT: Local–Global Collaborative Transformer for Fusion of Hyperspectral and Multispectral Images (TGRS 2024)

Official code repo for LGCT: Local–Global Collaborative Transformer for Fusion of Hyperspectral and Multispectral Images (TGRS'24) [PDF]

Wangquan He, Xiyou Fu, Nanying Li, Qi Ren and Sen Jia.

Network

Requirements

To install dependencies:

# create new anaconda env
conda create -n LGCT python=3.8 -y
conda activate LGCT

# install python dependencies
pip install -r requirements.txt

Usage:

Before training, you need to:

• Download Datasets : Pavia University / Houston / Chikusei.

• Set the data path -root in args_parser.py.

1. Training

Use the following command to train the network. Take training LGCT in Houston dataset as an example,

 CUDA_VISIBLE_DEVICES=0 python -u python.py  \
    -arch 'LGCT' \
    -root '[root path of datasets]' \
    -dataset 'Houston' \
    --model_path './checkpoints'\
    --n_epochs 10000 --lr 1e-4\
    --criterion 'L1' \

2. Testing

Before testing, set the pre-trained model weight 'pth' files to line 57 in test.py. These files are obtained through the training phase and can be found in the ./checkpoints.

Then run the following command:

python test.py

Citation

If you find this work helpful, please consider citing it. We would greatly appreciate it!

@article{he2024lgct,
  title={LGCT: Local-Global Collaborative Transformer for Fusion of Hyperspectral and Multispectral Images},
  author={He, Wangquan and Fu, Xiyou and Li, Nanying and Ren, Qi and Jia, Sen},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  year={2024},
  publisher={IEEE}
  volume={62},
  number={},
  pages={1-14}
}

Acknowledgment

Some codes are brought from BasicSR, SSRNET and BDT. We sincerely appreciate their contributions.