Sparse Matrix Transform for Hyperspectral Image Processing

@article{Theiler2011SparseMT,
  title={Sparse Matrix Transform for Hyperspectral Image Processing},
  author={James Theiler and Guangzhi Cao and Leonardo R. Bachega and Charles A. Bouman},
  journal={IEEE Journal of Selected Topics in Signal Processing},
  year={2011},
  volume={5},
  pages={424-437}
}
A variety of problems in remote sensing require that a covariance matrix be accurately estimated, often from a limited number of data samples. We investigate the utility of several variants of a recently introduced covariance estimator-the sparse matrix transform (SMT), a shrinkage-enhanced SMT, and a graph-constrained SMT-in the context of several of these problems. In addition to two more generic measures of quality based on likelihood and the Frobenius norm, we specifically consider weak… CONTINUE READING
Highly Cited
This paper has 40 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 27 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 23 references

Regularization for spectral matched filter and RX anomaly detector

  • N. M. Nasrabadi
  • Proc. SPIE, 2008, vol. 6966, p. 696604.
  • 2008
2 Excerpts

Similar Papers

Loading similar papers…