Code for "An Endmember-Oriented Transformer Network for Bundle-Based Hyperspectral Unmixing, TGRS, 2025"

EOT-Net leverages the advantages of endmember bundles to introduce variability while providing stable endmember results with clear physical meaning. We design an endmember-oriented Transformer (EOT) to capture endmember-specific features through directional subspace projection and a low-redundancy attention (LRA) mechanism. Subsequently, the proposed network is divided into two branches: endmember generation and abundance estimation, to process endmember-specific features. In the endmember generation branch, endmember-specific features are transformed into intraclass weights that are used to combine signatures within the bundles, and a set of endmembers is generated for each pixel. In the abundance estimation branch, endmember-specific features are integrated using a heterogeneous information fusion (HIF) module that leverages the spatial dis tribution heterogeneity of the endmembers, ultimately producing the abundance results.

If you use the code in your research, we would appreciate a citation to the original paper:

@ARTICLE{10843765,
  author={Xiang, Shu and Li, Xiaorun and Chen, Shuhan},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={An Endmember-Oriented Transformer Network for Bundle-Based Hyperspectral Unmixing}, 
  year={2025},
  volume={63},
  number={},
  pages={1-15},
  keywords={Feature extraction;Transformers;Hyperspectral imaging;Attention mechanisms;Estimation;Classification algorithms;Generators;Data mining;Change detection algorithms;Accuracy;Autoencoder (AE) network;endmember bundle;hyperspectral unmixing (HU);spectral variability (SV);Transformer},
  doi={10.1109/TGRS.2025.3530642}}