spectral.separability: spectral separability

In spatialEco: Spatial Analysis and Modelling Utilities

spectral separability

Description

Calculates spectral separability by class

Usage

spectral.separability(x, y, jeffries.matusita = TRUE)

Arguments

x	data.frame, matrix or vector of spectral values must, match classes defined in y
y	A vector or factor with grouping classes, must match row wise values in x
jeffries.matusita	(TRUE/FALSE) Return J-M distance (default) else Bhattacharyya

Details

Available statistics:

• Bhattacharyya distance (Bhattacharyya 1943; Harold 2003) measures the similarity of two discrete or continuous probability distributions.

• Jeffries-Matusita (default) distance (Bruzzone et al., 2005; Swain et al., 1971) is a scaled (0-2) version of Bhattacharyya. The J-M distance is asymptotic to 2, where 2 suggest complete separability.

Value

A matrix of class-wise Jeffries-Matusita or Bhattacharyya distance separability values

Author(s)

Jeffrey S. Evans [e-mail]

References

Bhattacharyya, A. (1943) On a measure of divergence between two statistical populations defined by their probability distributions'. Bulletin of the Calcutta Mathematical Society 35:99-109

Bruzzone, L., F. Roli, S.B. Serpico (1995) An extension to multiclass cases of the Jefferys-Matusita distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 33:1318-1321

Kailath, T., (1967) The Divergence and Bhattacharyya measures in signal selection. IEEE Transactions on Communication Theory 15:52-60

Examples

require(MASS)                
# Create example data 
d <- 6                 # Number of bands
n.class <- 5           # Number of classes
n <- rep(1000, 5)                
mu <- round(matrix(rnorm(d*n.class, 128, 1), 
            ncol=n.class, byrow=TRUE), 0)

x <- matrix(double(), ncol=d, nrow=0)
  classes <- integer()
    for (i in 1:n.class) {
      f <- svd(matrix(rnorm(d^2), ncol=d))
      sigma <- t(f$v) %*% diag(rep(10, d)) %*% f$v
      x <- rbind(x, mvrnorm(n[i], mu[, i], sigma))
      classes <- c(classes, rep(i, n[i]))
    }
colnames(x) <- paste0("band", 1:6)
classes <- factor(classes, labels=c("water", "forest",
                  "shrub", "urban", "ag"))

# Separability for multi-band (multivariate) spectra 
spectral.separability(x, classes)

# Separability for single-band (univariate) spectra 
spectral.separability(x[,1], classes)

spatialEco documentation built on Nov. 18, 2023, 1:13 a.m.