Spectral Unmixing via Data-Guided Sparsity

@article{Zhu2014SpectralUV,
  title={Spectral Unmixing via Data-Guided Sparsity},
  author={Feiyun Zhu and Ying Wang and Bin Fan and Shiming Xiang and Gaofeng Meng and Chunhong Pan},
  journal={IEEE Transactions on Image Processing},
  year={2014},
  volume={23},
  pages={5412-5427}
}
Hyperspectral unmixing, the process of estimating a common set of spectral bases and their corresponding composite percentages at each pixel, is an important task for hyperspectral analysis, visualization, and understanding. From an unsupervised learning perspective, this problem is very challenging-both the spectral bases and their composite percentages are unknown, making the solution space too large. To reduce the solution space, many approaches have been proposed by exploiting various… CONTINUE READING

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