Sparse Hyperspectral Unmixing Based on Constrained lp - l2 Optimization

@article{Chen2013SparseHU,
  title={Sparse Hyperspectral Unmixing Based on Constrained lp - l2 Optimization},
  author={Fen Chen and Yan Zhang},
  journal={IEEE Geoscience and Remote Sensing Letters},
  year={2013},
  volume={10},
  pages={1142-1146}
}
Linear spectral unmixing is an effective technique to estimate the abundances of materials present in each hyperspectral image pixel. Recently, sparse-regression-based unmixing approaches have been proposed to tackle this problem. Mostly, <i>l</i><sub>1</sub> norm minimization is used to approximate the <i>l</i><sub>0</sub> norm minimization problem in terms of computational complexity. In this letter, we model the hyperspectral unmixing as a constrained sparse <i>lp</i> - <i>l</i><sub>2</sub… CONTINUE READING
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