gdalcubes (version 0.7.0)

window_space: Apply a moving window (focal) operation or a convolution kernel over spatial dimensions of a data cube.

Description

Create a proxy data cube, which applies a convolution kernel or aggregation functions over two-dimensional moving windows sliding over spatial slices of a data cube. The function can either execute one or more predefined aggregation functions or apply a custom convolution kernel. Among others, use cases include image processing (edge detection, noise reduction, etc.) and enriching pixel values with local neighborhood properties (e.g. to use as predictor variables in ML models).

Usage

window_space(x, expr, ..., kernel, window, keep_bands = FALSE, pad = NA)

Value

proxy data cube object

Arguments

x

source data cube

expr

either a single string, or a vector of strings, defining which reducers will be applied over which bands of the input cube

...

optional additional expressions (if expr is not a vector)

kernel

two dimensional kernel (matrix) applied as convolution (with odd number of rows and columns)

window

integer vector with two elements defining the size (number of pixels) of the window in y and x direction, the total size of the window is window[1] * window[2]

keep_bands

logical; if FALSE (the default), original data cube bands will be dropped.

pad

padding method applied to the borders; use NULL for no padding (NA), a numeric a fill value, or one of "REPLICATE", "REFLECT", "REFLECT_PIXEL"

Details

The function either applies a kernel convolution (if the kernel argument is provided) or one or more built-in reducer function over moving windows.

In the former case, the kernel convolution will be applied over all bands of the input cube, i.e., the output cube will have the same number of bands as the input cubes.

To apply one or more aggregation functions over moving windows, the window argument must be provided as a vector with two integer sizes in the order y, x. Several string expressions can be provided to create multiple bands in the output cube. Notice that expressions have a very simple format: the reducer is followed by the name of a band in parentheses, e.g, "mean(band1)". Possible reducers include "min", "max", "sum", "prod", "count", "mean", "median", "var", and "sd".

Padding methods "REPLICATE", "REFLECT", "REFLEX_PIXEL" are defined according to https://openeo.org/documentation/1.0/processes.html#apply_kernel.

Examples

# create image collection from example Landsat data only 
# if not already done in other examples
if (!file.exists(file.path(tempdir(), "L8.db"))) {
  L8_files <- list.files(system.file("L8NY18", package = "gdalcubes"),
                         ".TIF", recursive = TRUE, full.names = TRUE)
  create_image_collection(L8_files, "L8_L1TP", file.path(tempdir(), "L8.db"), quiet = TRUE) 
}

L8.col = image_collection(file.path(tempdir(), "L8.db"))
v = cube_view(extent=list(left=388941.2, right=766552.4, 
                          bottom=4345299, top=4744931, t0="2018-04", t1="2018-06"),
              srs="EPSG:32618", nx = 497, ny=526, dt="P1M")
L8.cube = raster_cube(L8.col, v, chunking = c(1,1000,1000)) 
L8.cube = select_bands(L8.cube, c("B04", "B05")) 
L8.cube.mean5x5 = window_space(L8.cube, kernel = matrix(1/25, 5, 5))
L8.cube.mean5x5

# \donttest{
plot(L8.cube.mean5x5, key.pos=1)
# }

L8.cube.med_sd = window_space(L8.cube, "median(B04)" ,"sd(B04)", "median(B05)", "sd(B05)", 
                              window = c(5,5), keep_bands = TRUE)
L8.cube.med_sd
# \donttest{
plot(L8.cube.med_sd, key.pos=1)
# }