Pansharpening Datasets from WorldView 2, WorldView 3, QuickBird, Gaofen 2 sensors
[Chinese Webpage ]
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.
(1) The training and testing datasets for WorldView 3
:
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
(2) The training and testing datasets for QuickBird
:
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
(3) The training and testing datasets for Gaofen 2
:
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
(4) The testing datasets for WorldView 2
:
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!
(1) The training and testing datasets for WorldView 3
:
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
(2) The training and testing datasets for QuickBird
:
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
(3) The training and testing datasets for Gaofen 2
:
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
(4) The testing datasets for WorldView 2
:
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 ={ **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}
}
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