### Navigation

- index
- modules |
- PySptools 0.12.1 beta documentation »

# Detection classes

## ACE

class `detection.``ACE`

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

  `detect`(M, t, threshold=None)
    Parameters:
      M: numpy array
        A HSI cube (m x n x p).
      t: numpy array
        A target pixel (p).
    Returns: numpy array
      Vector of detector output (m x n x 1).
    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.
  `display`(whiteOnBlack=True, suffix=None)

    Plot the target map on a IPython Notebook.

    Parameters:
      whiteOnBlack: boolean [default True]
        By default, whiteOnBlack=True, the detected signal is white on a black background. You can invert this with whiteOnBlack=False.
      suffix: string [default None]
        Suffix to add to the title.
  `plot`(path, whiteOnBlack=True, suffix=None)

    Plot the target map.

    Parameters:
      path: string
        The path where to put the plot.
      whiteOnBlack: boolean [default True]
        By default, whiteOnBlack=True, the detected signal is white on a black background. You can invert this with whiteOnBlack=False.
      suffix: string [default None]
        Suffix to add to the file name.

## CEM

class `detection.``CEM`

  Performs the constrained energy minimization algorithm for target detection.

  `detect`(M, t, threshold=None)
    Parameters:
      M: numpy array
        A HSI cube (m x n x p).
      t: numpy array
        A target pixel (p).
    Returns: numpy array
      Vector of detector output (m x n x 1).
    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.
  `display`(whiteOnBlack=True, suffix=None)

    Plot the target map on a IPython Notebook.

    Parameters:
      whiteOnBlack: boolean [default True]
        By default, whiteOnBlack=True, the detected signal is white on a black background. You can invert this with whiteOnBlack=False.
      suffix: string [default None]
        Suffix to add to the title.
  `plot`(path, whiteOnBlack=True, suffix=None)

    Plot the target map.

    Parameters:
      path: string
        The path where to put the plot.
      whiteOnBlack: boolean [default True]
        By default, whiteOnBlack=True, the detected signal is white on a black background. You can invert this with whiteOnBlack=False.
      suffix: string [default None]
        Suffix to add to the file name.

## GLRT

class `detection.``GLRT`

  Performs the generalized likelihood test ratio algorithm for target detection.

  `detect`(M, t, threshold=None)
    Parameters:
      M: numpy array
        A HSI cube (m x n x p).
      t: numpy array
        A target pixel (p).
    Returns: numpy array
      Vector of detector output (m x n x 1).
    References
      T. F. AyouB, “Modified GLRT Signal Detection Algorithm,” IEEE Transactions on Aerospace and Electronic Systems, Vol 36, No 3, July 2000.
  `display`(whiteOnBlack=True, suffix=None)

    Plot the target map on a IPython Notebook.

    Parameters:
      whiteOnBlack: boolean [default True]
        By default, whiteOnBlack=True, the detected signal is white on a black background. You can invert this with whiteOnBlack=False.
      suffix: string [default None]
        Suffix to add to the title.
  `plot`(path, whiteOnBlack=True, suffix=None)

    Plot the target map.

    Parameters:
      path: string
        The path where to put the plot.
      whiteOnBlack: boolean [default True]
        By default, whiteOnBlack=True, the detected signal is white on a black background. You can invert this with whiteOnBlack=False.
      suffix: string [default None]
        Suffix to add to the file name.

## MatchedFilter

class `detection.``MatchedFilter`

  Performs the matched filter algorithm for target detection.

  `detect`(M, t, threshold=None)
    Parameters:
      M: numpy array
        A HSI cube (m x n x p).
      t: numpy array
        A target pixel (p).
    Returns: numpy array
      Vector of detector output (m x n x 1).
    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.
  `display`(whiteOnBlack=True, suffix=None)

    Plot the target map on a IPython Notebook.

    Parameters:
      whiteOnBlack: boolean [default True]
        By default, whiteOnBlack=True, the detected signal is white on a black background. You can invert this with whiteOnBlack=False.
      suffix: string [default None]
        Suffix to add to the title.
  `plot`(path, whiteOnBlack=True, suffix=None)

    Plot the target map.

    Parameters:
      path: string
        The path where to put the plot.
      whiteOnBlack: boolean [default True]
        By default, whiteOnBlack=True, the detected signal is white on a black background. You can invert this with whiteOnBlack=False.
      suffix: string [default None]
        Suffix to add to the file name.

## OSP

class `detection.``OSP`

  Performs the othogonal subspace projection algorithm for target detection.

  `detect`(M, E, t, threshold=None)
    Parameters:
      M: numpy array
        A HSI cube (m x n x p).
      E: numpy array
        Background pixels (n x p).
      t: numpy array
        A target pixel (p).
    Returns: numpy array
      Vector of detector output (m x n x 1).
    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.
  `display`(whiteOnBlack=True, suffix=None)

    Plot the target map on a IPython Notebook.

    Parameters:
      whiteOnBlack: boolean [default True]
        By default, whiteOnBlack=True, the detected signal is white on a black background. You can invert this with whiteOnBlack=False.
      suffix: string [default None]
        Suffix to add to the title.
  `plot`(path, whiteOnBlack=True, suffix=None)

    Plot the target map.

    Parameters:
      path: string
        The path where to put the plot.
      whiteOnBlack: boolean [default True]
        By default, whiteOnBlack=True, the detected signal is white on a black background. You can invert this with whiteOnBlack=False.
      suffix: string [default None]
        Suffix to add to the file name.

### Navigation

- index
- modules |
- PySptools 0.12.1 beta documentation »