Table Of Contents

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.