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# Detection functions

## ACE

`detection.detect.``ACE`(M, t)

  Performs the adaptive cosin/coherent estimator algorithm for target detection.

  Parameters:
    M: numpy array
      2d matrix of HSI data (N x p).
    t: numpy array
      A target endmember (p).
  Returns: numpy array
    Vector of detector output (N).
  References:
    X Jin, S Paswater, H Cline. “A Comparative Study of Target Detection Algorithms for Hyperspectral Imagery.” SPIE Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV. Vol 7334. 2009.

## CEM

`detection.detect.``CEM`(M, t)

  Performs the constrained energy minimization algorithm for target detection.

  Parameters:
    M: numpy array
      2d matrix of HSI data (N x p).
    t: numpy array
      A target endmember (p).
  Returns: numpy array
    Vector of detector output (N).
  References:
    Qian Du, Hsuan Ren, and Chein-I Cheng. A Comparative Study of Orthogonal Subspace Projection and Constrained Energy Minimization. IEEE TGRS. Volume 41. Number 6. June 2003.

## GLRT

`detection.detect.``GLRT`(M, t)

  Performs the generalized likelihood test ratio algorithm for target detection.

  Parameters:
    M: numpy array
      2d matrix of HSI data (N x p).
    t: numpy array
      A target endmember (p).
  Returns: numpy array
    Vector of detector output (N).
  References:
    T F AyouB, “Modified GLRT Signal Detection Algorithm,” IEEE Transactions on Aerospace and Electronic Systems, Vol 36, No 3, July 2000.

## MatchedFilter

`detection.detect.``MatchedFilter`(M, t)

  Performs the matched filter algorithm for target detection.

  Parameters:
    M: numpy array
      2d matrix of HSI data (N x p).
    t: numpy array
      A target endmember (p).
  Returns: numpy array
    Vector of detector output (N).
  References:

    > X Jin, S Paswater, H Cline. “A Comparative Study of Target Detection

    Algorithms for Hyperspectral Imagery.” SPIE Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV. Vol 7334. 2009.

## OSP

`detection.detect.``OSP`(M, E, t)

  Performs the othogonal subspace projection algorithm for target detection.

  Parameters:
    M: numpy array
      2d matrix of HSI data (N x p).
    E: numpy array
      2d matrix of background endmebers (p x q).
    t: numpy array
      A target endmember (p).
  Returns: numpy array
    Vector of detector output (N).
  References:
    Qian Du, Hsuan Ren, and Chein-I Cheng. “A Comparative Study of Orthogonal Subspace Projection and Constrained Energy Minimization.” IEEE TGRS. Volume 41. Number 6. June 2003.

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