Detection classes


ACE

class pysptools.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).
threshold: float or None [default None]
Apply a threshold to the detection result. Usefull to isolate the result.
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)

Display the target map to 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 pysptools.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).
threshold: float or None [default None]
Apply a threshold to the detection result. Usefull to isolate the result.
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)

Display the target map to 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 pysptools.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).
threshold: float or None [default None]
Apply a threshold to the detection result. Usefull to isolate the result.
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)

Display the target map to 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 pysptools.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).
threshold: float or None [default None]
Apply a threshold to the detection result. Usefull to isolate the result.
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)

Display the target map to 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 pysptools.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).
threshold: float or None [default None]
Apply a threshold to the detection result. Usefull to isolate the result.
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)

Display the target map to 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.