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This is a demo code for 'uDAS: An untied denoising autoencoder with sparsity for spectral unmixing', the code is for research purpose only, all rights reserved.
Contact information
Ying Qu: yqu3@vols.utk.edu
To run the code, please use 'train_auto_syn.m' in Matlab.
Please cite the following two paper.
Qu, Ying, and Hairong Qi. "uDAS: An untied denoising autoencoder with sparsity for spectral unmixing." IEEE Transactions on Geoscience and Remote Sensing 57.3 (2019): 1698-1712.
Qu, Ying, Rui Guo, and Hairong Qi. "Spectral unmixing through part-based non-negative constraint denoising autoencoder." 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2017.
@article{qu2019udas,
title={uDAS: An untied denoising autoencoder with sparsity for spectral unmixing},
author={Qu, Ying and Qi, Hairong},
journal={IEEE Transactions on Geoscience and Remote Sensing},
volume={57},
number={3},
pages={1698--1712},
year={2019},
publisher={IEEE}
}
@article{qu2017spectral,
title={Spectral unmixing through part-based non-negative constraint denoising autoencoder},
author={Qu, Ying and Guo, Rui and Qi, Hairong},
journal ={2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
pages={209--212},
year={2017},
organization={IEEE}
}
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Unsupervised spectral unmixing through an untied denoising autoencoder with sparsity (uDAS)
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