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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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