Nonlinear Unmixing of Hyperspectral Data Based on a Linear-Mixture/Nonlinear-Fluctuation Model

@article{Chen2013NonlinearUO,
  title={Nonlinear Unmixing of Hyperspectral Data Based on a Linear-Mixture/Nonlinear-Fluctuation Model},
  author={Jie Chen and C{\'e}dric Richard and Paul Honeine},
  journal={IEEE Transactions on Signal Processing},
  year={2013},
  volume={61},
  pages={480-492}
}
Spectral unmixing is an important issue to analyze remotely sensed hyperspectral data. Although the linear mixture model has obvious practical advantages, there are many situations in which it may not be appropriate and could be advantageously replaced by a nonlinear one. In this paper, we formulate a new kernel-based paradigm that relies on the assumption that the mixing mechanism can be described by a linear mixture of endmember spectra, with additive nonlinear fluctuations defined in a… CONTINUE READING
Highly Cited
This paper has 101 citations. REVIEW CITATIONS
76 Citations
46 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 76 extracted citations

101 Citations

0102030'13'14'15'16'17'18
Citations per Year
Semantic Scholar estimates that this publication has 101 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 46 references

Similar Papers

Loading similar papers…