Improved Deep Spectral Convolution Network For Hyperspectral Unmixing With Multinomial Mixture Kernel and Endmember Uncertainty

@article{zkan2018ImprovedDS,
  title={Improved Deep Spectral Convolution Network For Hyperspectral Unmixing With Multinomial Mixture Kernel and Endmember Uncertainty},
  author={Savas {\"O}zkan and Gozde Bozdagi Akar},
  journal={ArXiv},
  year={2018},
  volume={abs/1808.01104}
}
In this study, we propose a novel framework for hyperspectral unmixing by using a modular neural network structure while addressing the endmember uncertainty in our formulation. We present critical contributions throughout the manuscript: First, to improve the separability for hyperspectral data, we modify deep spectral convolution networks (DSCNs) that lead to more stable and accurate results. Second, we introduce a multinomial mixture kernel with a neural network (NN) which mimics the… CONTINUE READING

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