Sparse linear filters for detection and classification in hyperspectral imagery

@inproceedings{Theiler2006SparseLF,
  title={Sparse linear filters for detection and classification in hyperspectral imagery},
  author={James Theiler and Karen A. Glocer},
  booktitle={SPIE Defense + Commercial Sensing},
  year={2006}
}
We investigate the use of convex optimization to identify sparse linear filters in hyperspectral imagery. A linear filter is sparse if a large fraction of its coefficients are zero. A sparse linear filter can be advantageous because it only needs to access a subset of the available spectral channels, and it can be applied to high-dimensional data more cheaply than a standard linear detector. Finding good sparse filters is nontrivial because there is a combinatorially large number of discrete… CONTINUE READING

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