Total Variation Regularized Reweighted Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing

@article{He2017TotalVR,
  title={Total Variation Regularized Reweighted Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing},
  author={Wei He and Hongyan Zhang and Liangpei Zhang},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  year={2017},
  volume={55},
  pages={3909-3921}
}
Blind hyperspectral unmixing (HU), which includes the estimation of endmembers and their corresponding fractional abundances, is an important task for hyperspectral analysis. Recently, nonnegative matrix factorization (NMF) and its extensions have been widely used in HU. Unfortunately, most of the NMF-based methods can easily lead to an unsuitable solution, due to the nonconvexity of the NMF model and the influence of noise. To overcome this limitation, we make the best use of the structure of… CONTINUE READING

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