Remove or retain "noise" in a raster using PCA

denoise() applies a principal component analysis (PCA) to layers of a GRaster, then uses the PCA to predict values back to a raster. This retains only coarse-scale trends, thereby removing "noise" (locally extreme values that fall far from a PC axis).

noise() does the opposite by first constructing the PCA, predicting values back to the raster, then subtracting these values from the original, removing coarse-scale trends and thereby leaving "noise".

Usage

# S4 method for class 'GRaster'
denoise(x, scale = FALSE, percent = 80)

# S4 method for class 'GRaster'
noise(x, scale = FALSE, percent = 80)

Arguments

x

A GRaster with two or more layers.

scale

Logical: If TRUE, input layers will be rescaled by dividing each layer by its overall population standard deviation. Note that rasters will always be centered (have their mean subtracted from values). Centering and scaling is recommended when rasters values are in different units. The default is FALSE (do not scale).

percent

Numeric integer or integer in the range 50 to 99 (default is 80): Minimum total variation explained in the retained PC axes. Higher values will cause denoise() to remove less noise, and noise() to return less noise. If this value to too low to retain even one axis, the function will fail with a message to that effect.

Value

A multi-layer GRaster with one layer per input.

See also

pca(); stats::prcomp(); module i.pca in GRASS

Examples

if (grassStarted()) {

# Setup
library(terra)

# Climate raster:
madChelsa <- fastData("madChelsa")

# Convert a SpatRaster to a GRaster:
chelsa <- fast(madChelsa)

### Denoise:
quiet <- denoise(chelsa, scale = TRUE)

compare1 <- c(chelsa[["bio1"]], quiet[["bio1"]])
plot(compare1)

compare2 <- c(chelsa[["bio7"]], quiet[["bio7"]])
plot(compare2)

### Noise:
loud <- noise(chelsa, scale = TRUE)

compare1 <- c(chelsa[["bio1"]], loud[["bio1"]])
plot(compare1)

compare2 <- c(chelsa[["bio7"]], loud[["bio7"]])
plot(compare2)

}