Sparse Matrix Transform for Hyperspectral Image Processing

  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},
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
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