A faster way to trim a raster file

In the plot segmentation of image-based HTP platforms (e.g. UAV, ground-vehicle), we always need to trim a plot to remove the edge effect, then extracts the pixel values we are interesting. The operation is similar with cutline in gdalwarp command.

In R, raster package provides a function cellFromPolygon to extract cull numbers from a polygon. However, cellFromPolygon is slightly slow, especially for a larger file.

For example, the wheat plot from a 20M pixel Sony camera is about 3.4 MB. I want to trim this plot by 20% in each side.

library(raster)
## Loading required package: sp
## Warning: package 'sp' was built under R version 4.0.2
# Define the polygon to trim raster file 
ply <- read.table(textConnection('
434129.53992703213589 6950697.5565780829638
434127.33717670320766 6950693.9743350874633
434126.31413929269183 6950694.6008593635634
434128.51703721459489 6950698.1830912819132
434129.53992703213589 6950697.5565780829638'))
names(ply) <- c('long', 'lat')

plot <- brick('wheat-plot.tif')

# Plot the raster and polygon
plotRGB(plot)
lines(ply$long, ply$lat, col = 'red', lwd = 3)

The first method is to use cellFromPolygon to extrct the cell index.

library(sp)
# Create spatial polygon
sply <- SpatialPolygons(list(Polygons(list(Polygon(ply)), ID = '1')), 
                proj4string = plot@crs)

system.time(cell_idx1 <- cellFromPolygon(plot, sply))
##    user  system elapsed 
##    0.30    0.00    0.29

An alternative method is to use point.in.polygon function in sp package.

system.time({
    # Generate a grid of all x and y values in the raster file
    xy <- xyFromCell(plot, seq(1, prod(dim(plot)[1:2])))
    # Check point in the polygon
    cid <- point.in.polygon(xy[,1], xy[,2], ply[,1], ply[,2])
    # Obtain the cell index
    cell_idx2 <- seq(1, prod(dim(plot)[1:2]))[cid == 1]
})
##    user  system elapsed 
##    0.28    0.00    0.28

The first method takes about 10 S, but the second method only takes less than 1s. The two methods generate the same results for the cell index.

library(tidyverse)
## Warning: package 'tidyverse' was built under R version 4.0.2
## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --
## v ggplot2 3.3.2     v purrr   0.3.4
## v tibble  3.0.3     v dplyr   1.0.1
## v tidyr   1.1.1     v stringr 1.4.0
## v readr   1.3.1     v forcats 0.5.0
## Warning: package 'ggplot2' was built under R version 4.0.2
## Warning: package 'tibble' was built under R version 4.0.2
## Warning: package 'tidyr' was built under R version 4.0.2
## Warning: package 'dplyr' was built under R version 4.0.2
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x tidyr::extract() masks raster::extract()
## x dplyr::filter()  masks stats::filter()
## x dplyr::lag()     masks stats::lag()
## x dplyr::select()  masks raster::select()
pd <- xy %>% 
    as.data.frame() %>% 
    tbl_df() 
## Warning: `tbl_df()` is deprecated as of dplyr 1.0.0.
## Please use `tibble::as_tibble()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
pd %>%
    slice(cell_idx1[[1]]) %>% 
    ggplot() +
    geom_tile(aes(x, y), fill = 'red', alpha = 0.5) +
    geom_tile(aes(x, y), fill = 'blue', alpha = 0.5, 
              data = pd[cell_idx2,]) +
    coord_equal() + 
    theme_bw() +
    xlab('') + ylab('')

Bangyou Zheng
Bangyou Zheng
Data Scientist / Digital Agronomist

a research scientist of digital agriculture at the CSIRO.

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