Detection functions¶
ACE¶
-
pysptools.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¶
-
pysptools.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¶
-
pysptools.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¶
-
pysptools.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¶
-
pysptools.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.