GPU-Based Parallel Design of the Hyperspectral Signal Subspace Identification by Minimum Error (HySime)

@article{Wu2016GPUBasedPD,
  title={GPU-Based Parallel Design of the Hyperspectral Signal Subspace Identification by Minimum Error (HySime)},
  author={Xin Wu and Bormin Huang and Lizhe Wang and Jianqi Zhang},
  journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year={2016},
  volume={9},
  pages={4400-4406}
}
Signal subspace identification provides a performance improvement in hyperspectral applications, such as target detection, spectral unmixing, and classification. The HySime method is a well-known unsupervised approach for hyperspectral signal subspace identification. It computes the estimated noise and signal correlation matrices from which a subset of eigenvectors is selected to best represent the signal subspace in the least square sense. Depending on the complexity and dimensionality of the… CONTINUE READING

References

Publications referenced by this paper.
SHOWING 1-10 OF 29 REFERENCES

Hyperspectral Subspace Identification

  • IEEE Transactions on Geoscience and Remote Sensing
  • 2008
VIEW 9 EXCERPTS
HIGHLY INFLUENTIAL

Real-Time Identification of Hyperspectral Subspaces

  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • 2014

GPU-Accelerated Computation for Electromagnetic Scattering of a Double-Layer Vegetation Model

  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • 2013

Hyperspectral Unmixing on GPUs and Multi-Core Processors: A Comparison

  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • 2013
VIEW 1 EXCERPT

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