-
Recommendation:
Use the code-toolbox [DLPan-Toolbox] + the dataset [PanCollection] for fair training and testing! -
Also, a dataset [HyperPanCollection] for another similar task, i.e., hyperspectral pansharpening!
-
Latest Update (Dec. 11, 2022):
we updated full-resolution test examples that contain more different imgae scenes. -
Latest Update (Mar. 20, 2023):
one testing example in reduce-resolution format for WV3 sensor is not consistent with the one in full-resolution format, we have fixed it.
WorldView 3 Dataset | Link | Size |
---|---|---|
Training Dataset | [download link] | 5.76GB |
Testing Dataset (ReducedData, H5 Format) | [download link] | 20 examples |
Testing Dataset (FullData, H5 Format) | [download link] | 20 examples |
Testing Dataset (ReducedData, mat Format) | [download link] | 20 examples |
Testing Dataset (FullData, mat Format) | [download link] | 20 examples |
Note: H5 files have same data with mat files (but with different formats) which can be used for single image test
QuickBird Dataset | Link | Size |
---|---|---|
Training Dataset | [download link] | 5.37GB |
Testing Dataset (ReducedData, H5 Format) | [download link] | 20 examples |
Testing Dataset (FullData, H5 Format) | [download link] | 20 examples |
Testing Dataset (ReducedData, mat Format) | [download link] | 20 examples |
Testing Dataset (FullData, mat Format) | [download link] | 20 examples |
Gaofen 2 Dataset | Link | Size |
---|---|---|
Training Dataset | [download link] | 6.21GB |
Testing Dataset (ReducedData, H5 Format) | [download link] | 20 examples |
Testing Dataset (FullData, H5 Format) | [download link] | 20 examples |
Testing Dataset (ReducedData, mat Format) | [download link] | 20 examples |
Testing Dataset (FullData, mat Format) | [download link] | 20 examples |
WorldView 2 Dataset | Link | Size |
---|---|---|
Testing Dataset (ReducedData, H5 Format) | [download link] | 20 examples |
Testing Dataset (FullData, H5 Format) | [download link] | 20 examples |
Testing Dataset (ReducedData, mat Format) | [download link] | 20 examples |
Testing Dataset (FullData, mat Format) | [download link] | 20 examples |
Note: This data is only used for the test of network generalization, thus no training dataset!
- Use [Baidu Cloud] to download these datasets.
WorldView 3 Dataset | Link | Size |
---|---|---|
Training Dataset | [download link] | 5.76GB |
Testing Dataset (ReducedData, H5 Format) | [download link] | 20 examples |
Testing Dataset (FullData, H5 Format) | [download link] | 20 examples |
Testing Dataset (ReducedData, mat Format) | [download link] | 20 examples |
Testing Dataset (FullData, mat Format) | [download link] | 20 examples |
QuickBird Dataset | Link | Size |
---|---|---|
Training Dataset | [download link] | 5.37GB |
Testing Dataset (ReducedData, H5 Format) | [download link] | 20 examples |
Testing Dataset (FullData, H5 Format) | [download link] | 20 examples |
Testing Dataset (ReducedData, mat Format) | [download link] | 20 examples |
Testing Dataset (FullData, mat Format) | [download link] | 20 examples |
Gaofen 2 Dataset | Link | Size |
---|---|---|
Training Dataset | [download link] | 6.21GB |
Testing Dataset (ReducedData, H5 Format) | [download link] | 20 examples |
Testing Dataset (FullData, H5 Format) | [download link] | 20 examples |
Testing Dataset (ReducedData, mat Format) | [download link] | 20 examples |
Testing Dataset (FullData, mat Format) | [download link] | 20 examples |
WorldView 2 Dataset | Link | Size |
---|---|---|
Testing Dataset (ReducedData, H5 Format) | [download link] | 20 examples |
Testing Dataset (FullData, H5 Format) | [download link] | 20 examples |
Testing Dataset (ReducedData, mat Format) | [download link] | 20 examples |
Testing Dataset (FullData, mat Format) | [download link] | 20 examples |
Note: This data is only used for the test of network generalization, thus no training dataset!
More details about the similation procedure of datasets, you may check the following two papers:
@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}
}
and
@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}
}
We are glad to hear from you. If you have any questions, please feel free to contact wxwsx1997@qq.com.