R/rspectrum.R

In foto: Fourier Transform Textural Ordination

#' Calculates a radial spectrum
#'
#' This is an internal function and not to be used stand-alone.
#'
#' @param x a square matrix
#' @param w a moving window size
#' @param n normalize, bolean \code{TRUE} or \code{FALSE}
#' @param h high pass filter on the two first spectra values
#'  set to 0, limits the influence of low frequency components
#'  bolean \code{TRUE} or \code{FALSE}
#' @param env local environment to evaluate
#' @param ... additional parameters to forward
#' @return Returns a radial spectrum values for the image used
#' in order to classify texture using a PCA (or other) analysis.

rspectrum <- function(x,
                      w,
                      n = TRUE,
                      h = TRUE,
                      env,
                      ...) {
  # increment
  i <- get("i", envir = env)

  # counter increment
  assign("i", i + 1, envir = env)

  # check if variable x is square
  # we need to pass the window size
  # as we can't deduce this from the number of values alone
  # as the values are passed as a vector not a matrix
  if (w == sqrt(length(x))) {
    # convert to square matrix
    im <- matrix(x, w, w)

    # extract the squared amplitude of the FFT
    fftim <- Mod(stats::fft(im))^2

    # calculate distance from the center of the image
    offset <- ceiling(dim(im)[1] / 2)

    # define distance matrix
    # r is rounded to an integer by zonal
    r <- sqrt((col(im) - offset)^2 + (row(im) - offset)^2)

    # suppress warning on missing extent (which
    # is normal)
    fftim <- suppressWarnings(terra::rast(fftim))
    r <- suppressWarnings(terra::rast(r))

    # calculate the mean spectrum for distances r
    # (note: reverse the order of the results to center
    # the FFT spectrum)
    rspec <- rev(
      terra::zonal(
        fftim,
        r,
        fun = "mean",
        na.rm = TRUE
      )[, 2]
    )

    if (n) {
      # Normalize by dividing with the image standard deviation
      # publictions are inconsistent on the use of normalization
      # need to clarify this better.
      rspec <- rspec / stats::sd(im, na.rm = TRUE)
    }

    # set first two values to 0 these are inherent to the
    # structure of the image
    if (h) {
      rspec[1:2] <- 0
    }

    # only use the first 29 useful harmonics of the r-spectrum
    # in accordance to ploton et al. 2012
    output <- get("output", envir = env)

    if (w / 2 < 29) {
      output[i, ] <- rspec[1:floor(w / 2)]
    } else {
      output[i, ] <- rspec[1:29]
    }

    # assign values to matrix
    assign("output", output, envir = env)

    # always return i (index for mapping)
    return(i)
  } else {
    # if not square return i as well (index for mapping)
    return(i)
  }
}