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Quantizes raster to a set number of discrete levels

Source: R/quantize_raster.R
quantize_raster.Rd

Quantizes raster to a set number of discrete levels starting at 0. There are 2 methods of quantization are available: "equal range" and "equal prob"

Usage

quantize_raster(
  r,
  n_levels,
  quant_method = NULL,
  min_val = NULL,
  max_val = NULL,
  maxcell = Inf,
  filename = NULL,
  overwrite = FALSE,
  method = NULL,
  wopt = list()
)

Arguments

r

A single layer SpatRaster, RasterLayer, or matrix.

n_levels

number of levels to quantize to

quant_method

quantization method (either "range" or "prob"). "range" quantization will create bins that cover a range of equal size. "prob" performs equal probability quantization and will use quantiles to create bins with approximately equal number of samples.

min_val

minimum value for equal range quantization (if not supplied, the minimum value of the raster is used)

max_val

maximum value for equal range quantization (if not supplied, the maximum value of the raster is used)

maxcell

positive integer used to take a regular sample of x if "prob" is used as 'quant_method' (default is Inf)

filename

character Output filename.

overwrite

logical. If TRUE, filename is overwritten (default is FALSE).

method

deprecated. Use 'quant_method'

wopt

list with named options for writing files as in writeRaster

Value

a single layer SpatRaster or RasterLayer with integer values ranging from 0 to n_levels-1

Details

Equal probability quantization is the method recommended in Haralick et al., 1973. However, equal range may be more desirable if making comparisons across several different rasters where you need the gray levels to correspond in a consistent way to the original data, as you can supply the global max/min or the theoretical max/min values that could occur. When equal probability quantization is used, quantiles are generated using type 8 as recommended by Hyndman and Fan (1996). This method provides estimates that are approximately median-unbiased regardless of the distribution of x.

References

Haralick, R.M., Shanmugam, K., Dinstein, I., 1973. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics 610–621. https://doi.org/10.1109/TSMC.1973.4309314

Hyndman, R.J., Fan, Y., 1996. Sample Quantiles in Statistical Packages. The American Statistician 50, 361–365. https://doi.org/10.1080/00031305.1996.10473566

Examples

r<- rast(volcano, extent= ext(2667400, 2667400 + ncol(volcano)*10,
6478700, 6478700 + nrow(volcano)*10),
crs = "EPSG:27200")
rq1 <- quantize_raster(r = r, n_levels = 16, quant_method = "prob")
rq2 <- quantize_raster(r = r, n_levels = 16, quant_method = "range")