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84 lines
4 KiB
R
84 lines
4 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/window.R
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\name{window_space}
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\alias{window_space}
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\title{Apply a moving window (focal) operation or a convolution kernel over spatial dimensions of a data cube.}
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\usage{
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window_space(x, expr, ..., kernel, window, keep_bands = FALSE, pad = NA)
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}
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\arguments{
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\item{x}{source data cube}
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\item{expr}{either a single string, or a vector of strings, defining which reducers will be applied over which bands of the input cube}
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\item{...}{optional additional expressions (if expr is not a vector)}
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\item{kernel}{two dimensional kernel (matrix) applied as convolution (with odd number of rows and columns)}
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\item{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]}
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\item{keep_bands}{logical; if FALSE (the default), original data cube bands will be dropped.}
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\item{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"}
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}
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\value{
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proxy data cube object
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}
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\description{
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Create a proxy data cube, which applies a convolution kernel or aggregation functions over two-dimensional moving
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windows sliding over spatial slices of a data cube. The function can either execute one or more predefined aggregation functions or
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apply a custom convolution kernel. Among others, use cases include image processing (edge detection, noise reduction, etc.) and
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enriching pixel values with local neighborhood properties (e.g. to use as predictor variables in ML models).
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}
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\details{
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The function either applies a kernel convolution (if the \code{kernel} argument is provided) or one or more built-in reducer function
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over moving windows.
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In the former case, the kernel convolution will be applied over all bands of the input
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cube, i.e., the output cube will have the same number of bands as the input cubes.
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To apply one or more aggregation functions over moving windows, the window argument must be provided as a vector with two integer sizes
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in the order y, x. Several string expressions can be provided to create multiple bands in the output cube.
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Notice that expressions have a very simple format: the reducer is followed by the name of a band in parentheses, e.g, "mean(band1)".
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Possible reducers include "min", "max", "sum", "prod", "count", "mean", "median", "var", and "sd".
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Padding methods "REPLICATE", "REFLECT", "REFLEX_PIXEL" are defined according to
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\url{https://openeo.org/documentation/1.0/processes.html#apply_kernel}.
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}
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\note{
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Implemented reducers will ignore any NAN values (as \code{na.rm = TRUE} does).
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Calling this function consecutively many times may result in long computation times depending on chunk and window sizes due to the need to read adjacent data cube chunks.
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This function returns a proxy object, i.e., it will not start any computations besides deriving the shape of the result.
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}
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\examples{
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# create image collection from example Landsat data only
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# if not already done in other examples
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if (!file.exists(file.path(tempdir(), "L8.db"))) {
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L8_files <- list.files(system.file("L8NY18", package = "gdalcubes"),
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".TIF", recursive = TRUE, full.names = TRUE)
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create_image_collection(L8_files, "L8_L1TP", file.path(tempdir(), "L8.db"), quiet = TRUE)
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}
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L8.col = image_collection(file.path(tempdir(), "L8.db"))
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v = cube_view(extent=list(left=388941.2, right=766552.4,
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bottom=4345299, top=4744931, t0="2018-04", t1="2018-06"),
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srs="EPSG:32618", nx = 497, ny=526, dt="P1M")
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L8.cube = raster_cube(L8.col, v, chunking = c(1,1000,1000))
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L8.cube = select_bands(L8.cube, c("B04", "B05"))
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L8.cube.mean5x5 = window_space(L8.cube, kernel = matrix(1/25, 5, 5))
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L8.cube.mean5x5
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\donttest{
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plot(L8.cube.mean5x5, key.pos=1)
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}
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L8.cube.med_sd = window_space(L8.cube, "median(B04)" ,"sd(B04)", "median(B05)", "sd(B05)",
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window = c(5,5), keep_bands = TRUE)
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L8.cube.med_sd
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\donttest{
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plot(L8.cube.med_sd, key.pos=1)
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}
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}
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