This repository is the official PyTorch implementation of our paper Towards Robust Pansharpening: A Large-Scale High-Resolution Multi-Scene Dataset and Novel Approach.

Requirements

pip install -r requirements.txt

Dataset

• Google Drive | 123 Cloud Drive | HuggingFace

PanBench
├─GF1
│  ├─NIR_256
│  ├─PAN_1024
│  └─RGB_256
├─GF2
│  ├─NIR_256
│  ├─PAN_1024
│  └─RGB_256
├─GF6
│  ├─NIR_256
│  ├─PAN_1024
│  └─RGB_256
├─IN
│  ├─NIR_256
│  ├─PAN_1024
│  └─RGB_256
├─LC7
│  ├─NIR_256
│  ├─PAN_1024
│  └─RGB_256
├─LC8
│  ├─NIR_256
│  ├─PAN_1024
│  └─RGB_256
├─QB
│  ├─NIR_256
│  ├─PAN_1024
│  └─RGB_256
├─WV2
│  ├─NIR_256
│  ├─PAN_1024
│  └─RGB_256
├─WV3
│  ├─NIR_256
│  ├─PAN_1024
│  └─RGB_256
└─WV4
    ├─NIR_256
    ├─PAN_1024
    └─RGB_256

Training

python src/train.py experiment=cmfnet

Evaluation

python src/eval.py experiment=cmfnet

Pre-trained Models

You can download pre-trained models in logs/train/runs.

Overview of Model Zoo and Datasets

• Supported methods.

  • PNN (Remote Sensing'2016)
  • PanNet (ICCV'2017)
  • MSDCNN (J-STARS'2018)
  • TFNet (Inform. Fusion'2020)
  • FusionNet (TGRS'2020)
  • GPPNN (CVPR'2021)
  • SRPPNN (TGRS'2021)
  • PGCU (CVPR'2023)
  • CMFNet (Remote Sensing'2024)

• Supported satellites.

  • GaoFen1
  • GaoFen2
  • GaoFen6
  • Landsat7
  • Landsat8
  • WorldView2
  • WorldView3
  • WorldView4
  • QuickBird
  • IKONOS

Visualization

python visualize.py

Citation

If you use our code or models in your research, please cite with:

@Article{cmfnet,
AUTHOR = {Wang, Shiying and Zou, Xuechao and Li, Kai and Xing, Junliang and Cao, Tengfei and Tao, Pin},
TITLE = {Towards Robust Pansharpening: A Large-Scale High-Resolution Multi-Scene Dataset and Novel Approach},
JOURNAL = {Remote Sensing},
VOLUME = {16},
YEAR = {2024},
NUMBER = {16},
}