EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixing

• Download datasets from (new link) https://drive.google.com/file/d/0B1kqE05deSqxYm8wS1F3UXZYeWM/view?usp=drivesdk&resourcekey=0-NGlaHn82mfJaM9QNXb-cKA
• Extract datasets.zip to project directory.
• Run demo.m to visualize results for a selected dataset.

Code will be released soon on this page. Stay tuned!...

Our Results

Samson Data and Abundance Map Results

Jasper Data and Abundance Map Results

Urban Data and Abundance Map Results

Cuprite Data and Abundance Map Results

Pavia University Data and Abundance Map Results

Pavia Center Data and Abundance Map Results

Gulfport Data and Abundance Map Results

IEEE 2013 Challenge Data and Abundance Map Results

DC Data and Abundance Map Results

References

Please cite the following paper:

[1] Savas Ozkan, Berk Kaya and Gozde Bozdagi Akar, EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixing, IEEE Transactions on Geoscience and Remote Sensing, 2018:

@article{ozkan2018endnet,
  title={Endnet: Sparse autoencoder network for endmember extraction and hyperspectral unmixing},
  author={Ozkan, Savas and Kaya, Berk and Akar, Gozde Bozdagi},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  volume={57},
  number={1},
  pages={482--496},
  year={2018},
  publisher={IEEE}
}

[2] Savas Ozkan and Gozde Bozdagi Akar, Spectral Unmixing With Multinomial Mixture Kernel and Wasserstein Generative Adversarial Loss, Advances in Neural Information Processing Systems Workshops, 2020:

@article{ozkan2020spectral,
  title={Spectral Unmixing With Multinomial Mixture Kernel and Wasserstein Generative Adversarial Loss},
  author={Ozkan, Savas and Akar, Gozde Bozdagi},
  journal={arXiv preprint arXiv:2012.06859},
  year={2020}
}