✨ PanCollection

🤗 To get started with PanCollection benchmark (training, inference, etc.), we recommend reading Google Colab!

Recommendations

We recommend users to use the code-toolbox DLPan-Toolbox + the dataset PanCollection for fair training and testing!

Deploy

PanCollection has provided complete packages.

pip install pancollection --upgrade

How to Get Started with the Model

import pancollection as pan
cfg = pan.TaskDispatcher.new(task='pansharpening', mode='entrypoint', arch='FusionNet', 
                             dataset_name="gf2", use_resume=False,
                             dataset={'train': 'gf2', 'test': 'test_gf2_multiExm1.h5'})
print(pan.TaskDispatcher._task)
pan.trainer.main(cfg, pan.build_model, pan.getDataSession)

Training Details

See Google Colab for quick start.

See Github Project for coding details.

Evaluation

See the Leaderboard for model results.

See the PanCollection Paper for early results.

Satellite	Value	Comment
WorldView-3	2047
QuickBird	2047
GaoFen-2	1023
WorldView-2	2047

Citation

To learn more about the PanCollection dataset, see the Github Pages.

@ARTICLE{dengjig2022,
    author={邓良剑，冉燃，吴潇，张添敬},
    journal={中国图象图形学报},
    title={遥感图像全色锐化的卷积神经网络方法研究进展},
     year={2022},
      volume={},
      number={9},
      pages={},
      doi={10.11834/jig.220540}
   }

@ARTICLE{deng2022vivone,
    author={L. -J. Deng, G. Vivone, M. E. Paoletti, G. Scarpa, J. He, Y. Zhang, J. Chanussot, and A. Plaza},
    journal={IEEE Geoscience and Remote Sensing Magazine}, 
    title={Machine Learning in Pansharpening: A Benchmark, from Shallow to Deep Networks}, 
    year={2022},
    volume={10},
    number={3},
    pages={279-315},
    doi={10.1109/MGRS.2022.3187652}
   }

License

PanCollection is made available under the GPLv2.0 license.

Contact

[email protected]

[email protected]