Automatic Target Generation Process endmembers induction algorithm
Parameters:
data: numpy array
2d matrix of HSI data ((m x n) x p)
q: int
Number of endmembers to be induced (positive integer > 0)
Returns: tuple: numpy array, numpy array
Set of induced endmembers (N x p).
Induced endmembers indexes vector.
References:
A. Plaza, C.-I. Chang, “Impact of Initialization on Design of Endmember
Extraction Algorithms”, Geoscience and Remote Sensing, IEEE Transactions on,
vol. 44, no. 11, pgs. 3397-3407, 2006.
eea.nfindr.NFINDR(data, q, transform=None, maxit=None, ATGP_init=False)¶
N-FINDR endmembers induction algorithm.
Parameters:
data: numpy array
Column data matrix [nvariables x nsamples].
q: int
Number of endmembers to be induced.
transform: numpy array [default None]
The transformed ‘data’ matrix by MNF (N x components). In this
case the number of components must == q-1. If None, the built-in
call to PCA is used to transform the data.
maxit: int [default None]
Maximum number of iterations. Default = 3*q.
ATGP_init: boolean [default False]
Use ATGP to generate the first endmembers set instead
of a random selection.
Returns: tuple: numpy array, numpy array, int
Set of induced endmembers (N x p)
Set of transformed induced endmembers (N x p)
Array of indices into the array data corresponding to the
induced endmembers
The number of iterations.
References:
Winter, M. E., “N-FINDR: an algorithm for fast autonomous spectral
end-member determination in hyperspectral data”, presented at the Imaging
Spectrometry V, Denver, CO, USA, 1999, vol. 3753, pgs. 266-275.