Hyperspectral unmixing task aims to address the complex spectral mixtures in hyperspectral data by decomposing each pixel’s spectral signature into pure spectral signatures ( endmembers ) and their corresponding proportions ( abundances ).

This repo contains the Urban dataset. You can download it by Baidu Drive or Google Drive.

Predicting the abundance maps and the endmemebers by reconstructing the hyperpsectral patches with the tailored auto-encoder based model.
trainval.py

Note: 1) please download the pretrained checkpoint pth :
Spatial_MAE ViT-B;
Spectral_MAE ViT-B;
2) please download the hyperspectral unmixing dataset;
3) please put the pretrained model file and dataset in the file './data/';
Please see func.get_args for more details .

SpatSIGMA_Unmix
HyperSIGMA_Unmix

This project is partly based on CNNAEU and DeepTrans.
Thanks for their wonderful work!