Detection¶
This module supports the follwing detection algorithms. Each of these algorithms come in two flavors, as functions or as classes.
- Adaptive Cosin/coherent Estimator (ACE)
- Constrained Energy Minimization (CEM)
- Generalized Likelihood Ratio Test (GLRT)
- MatchedFilter
- Othogonal Subspace Projection (OSP)
See also
See the file test_detect.py for an example.
Adaptive Cosin/coherent Estimator (ACE)¶
Function¶
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pysptools.detection.detect.ACE(M, t)[source]¶ 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.
Class¶
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class
pysptools.detection.ACE[source]¶ Performs the adaptive cosin/coherent estimator algorithm for target detection.
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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.
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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.
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Constrained Energy Minimization (CEM)¶
Function¶
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pysptools.detection.detect.CEM(M, t)[source]¶ 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.
Class¶
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class
pysptools.detection.CEM[source]¶ Performs the constrained energy minimization algorithm for target detection.
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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.
-
Generalized Likelihood Ratio Test (GLRT)¶
Function¶
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pysptools.detection.detect.GLRT(M, t)[source]¶ 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.
Class¶
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class
pysptools.detection.GLRT[source]¶ Performs the generalized likelihood test ratio algorithm for target detection.
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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¶
Function¶
-
pysptools.detection.detect.MatchedFilter(M, t)[source]¶ 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.
Class¶
-
class
pysptools.detection.MatchedFilter[source]¶ 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.
-
Othogonal Subspace Projection (OSP)¶
Function¶
-
pysptools.detection.detect.OSP(M, E, t)[source]¶ 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.
Class¶
-
class
pysptools.detection.OSP[source]¶ Performs the othogonal subspace projection algorithm for target detection.
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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.
-