The main process of the proposed UAL framework contains two steps, first is use the "Train_FusionModel.py" to train the Fusionmodel. Second, we utilize the pre-trained Fusionmodel to provide the initial input of the adaptor network and then we train the adaptor under unsupervised mode to generate the specific reconstructed HR HSI.
The main process of the proposed UTAL framework is same as UAL. To implement the meta-al-UTAL, you need to train the fusion model with "meta-al-UTAL/Train_FusionModel.py". Then, you should conduct meta train on the adaptor. At last, optimizing the adaptor to fit different testing samples.