sits (version 1.5.4)

plot.probs_vector_cube: Plot probability vector cubes

Description

Plots a probability vector cube, which result from first running a segmentation sits_segment and then running a machine learning classification model. The result is a set of polygons, each with an assigned probability of belonging to a specific class.

Usage

# S3 method for probs_vector_cube
plot(
  x,
  ...,
  tile = x[["tile"]][[1L]],
  roi = NULL,
  labels = NULL,
  palette = "YlGn",
  rev = FALSE,
  scale = 1,
  legend_position = "outside"
)

Value

A plot containing probabilities associated to each class for each pixel.

Arguments

x

Object of class "probs_vector_cube".

...

Further specifications for plot.

tile

Tile to be plotted.

roi

Region of interest (see notes below).

labels

Labels to plot

palette

RColorBrewer or "cols4all" palette

rev

Reverse order of colors in palette?

scale

Scale to plot map (0.4 to 1.0)

legend_position

Where to place the legend (default = "outside")

Author

Gilberto Camara,

Examples

if (sits_run_examples()) {
    # create a random forest model
    rfor_model <- sits_train(samples_modis_ndvi, sits_rfor())
    # create a data cube from local files
    data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
    cube <- sits_cube(
        source = "BDC",
        collection = "MOD13Q1-6.1",
        data_dir = data_dir
    )
    # segment the image
    segments <- sits_segment(
        cube = cube,
        seg_fn = sits_snic(
            grid_seeding = "diamond",
            spacing = 7,
            compactness = 0.5,
            padding = 0
        ),
        output_dir = tempdir()
    )
    # classify a data cube
    probs_vector_cube <- sits_classify(
        data = segments,
        ml_model = rfor_model,
        output_dir = tempdir()
    )
    # plot the resulting probability cube
    plot(probs_vector_cube)
}