General Interface to N-FINDR Spectral Unmixing Implementations

All the N-FINDR techniques are based on the fact that, in N spectral dimensions, the N-volume contained by a simplex formed of the purest pixels is larger than any other volume formed from any other combination of pixels.

nfindr(
  x,
  p,
  init = c("projections", "random", "coordinates_sequence", "coordinates_random"),
  iter = c("points", "endmembers", "both"),
  estimator = c("Cramer", "volume", "height", "cofactor", "LDU"),
  iter_max = 10,
  n_init = 1,
  ...
)

Arguments

x

Data to unmix (spectra in rows). It will be converted to a matrix using as.matrix. The matrix should contain a spectrum per row. It is recommended to reduce the dimensionality of the data to `p-1` before using this function. This can be done using PCA or other dimensionality reduction techniques. Withouth dimensionality reduction, the results might be inefficient and computation intensive.

p

Number of endmembers.

init

Initialization strategy.

vector of `p` integers - manually selected initial points, can be output of previous another endmember extraction method, e.g. VCA
list of numeric vectors - multiple initializations, each element should be a vector of `p` integers representing indices
function - a callable function that takes (data, p) as arguments and returns either numeric indices or a list with 'indices' element (e.g. output of other methods)
random - randomly selected points
projections - selecting the two extreme points of the projections of the data onto random vectors
coordinates_sequence - selecting extreme points along coordinate axes in a deterministic sequential manner
coordinates_random - selecting extreme points along coordinate axes with randomization

Default: "projections" is used.

iter

The iteration strategy. Options: "points", "endmembers", "both". By default, "points" are used.

estimator

Volume change estimator

volume - straight forward volume calculation without any optimization
height - Use the fact that the simplex volume is proportional to the product of `height` and `base volume`.
Cramer - Using Cramer's rule
LDU - Using LDU matrix decomposition
cofactor - Using the cofactor expansion for calculating `det(E)`

Default: Cramer's rules is used since it has best performance.

iter_max

Maximum number of iterations to make.

n_init

Number of initializations to try. The final result will be the best output of all initializations. Ignored if specific initial endmember indices provided. Default: 1.

...

Additional parameters for the methods (currently unused).

Value

A list which contains:

indices: the indices of the spectra which increased the simplex volume the most. These are the indices of the endmembers.
iterations_count: if debug level higher than 0, number of loop iterations.
replacements_count: if debug level higher than 0, number of actual replacements during iterations.
replacements: if debug level higher than 1, the vectors of indices at all replacement steps. If fact, is used to see how the simplex was growing.

Examples

data("demo_data")

# Reduce data dimensionality with PCA
pca <- prcomp(demo_data)
x <- pca$x[,1:2]

# Perform N-FINDR in reduced space
nf <- nfindr(x, p = 3)

# Get endmembers both in reduced and original space
ems <- endmembers(nf, demo_data)
ems_pca <- endmembers(nf, x)

# Plot endmembers
matplot(t(ems), type = "l")

General Interface to N-FINDR Spectral Unmixing Implementations

Arguments

Value

See also

Examples