P2Sharpen

Code of P2Sharpen: A progressive pansharpening network with deep spectral transformation.

Running environment :

python=3.8, pytorch-gpu=1.7.1, matlab = 2018a.

Preparation:

Construct the train, validation, test dataset according to the Wald protocol.
Put the all the dataset in the root directory, namely TrainFolder, ValidFolder and TestFolder.
In each directory, there are four subdirectories, namely pan_label/ ms_label/ pan/ ms/
The images in each directory should correspond to each other.

To train :

The whole training process contains two part, STNet and P2Net.
Please run "transfertrain.py" to learn the spectral tranformation network(STNet).
TNet guides the optimization of P2Net, so ensuring the accuracy before the next step.
Please run "fusiontrain.py" to learn the progressive pansharpening network (P2Net).

To valid :

Use the functions in the file ".\Eval.py" or others to evalute the performance on valid dataset.
Pick out the best parameters and save it in path "./Model/P2Net/fusion.pth".

To test :

Run the "fusionpredict.py" to generate the pansharpening results.
Open the Matlab and run the file ".\Evalution\FusionEval.m".