plot.class_vector_cube: Plot Segments

In sits: Satellite Image Time Series Analysis for Earth Observation Data Cubes

Plot Segments

Description

Plot vector classified cube

Usage

## S3 method for class 'class_vector_cube'
plot(
  x,
  ...,
  tile = x[["tile"]][[1L]],
  legend = NULL,
  roi = NULL,
  seg_color = "black",
  line_width = 0.5,
  palette = "Spectral",
  scale = 1,
  legend_position = "outside"
)

Arguments

x	Object of class "segments".
...	Further specifications for plot.
tile	Tile to be plotted.
legend	Named vector that associates labels to colors.
roi	Region of interest (see note)
seg_color	Segment color.
line_width	Segment line width.
palette	A RColorBrewer or "cols4all" palette
scale	Scale to plot map (0.4 to 1.0)
legend_position	Where to place the legend (default = "outside")

Value

A plot object with an RGB image or a B/W image on a color scale using the chosen palette

Note

To see which color palettes are supported, please run cols4all::c4a_gui().

To define a roi use one of:

• A path to a shapefile with polygons;

• A sfc or sf object from sf package;

• A SpatExtent object from terra package;

• A named vector ("lon_min", "lat_min", "lon_max", "lat_max") in WGS84;

• A named vector ("xmin", "xmax", "ymin", "ymax") with XY coordinates.

Author(s)

Gilberto Camara, [e-mail]

Examples

if (sits_run_examples()) {
    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,
        output_dir = tempdir()
    )
    # create a classification model
    rfor_model <- sits_train(samples_modis_ndvi, sits_rfor())
    # classify the segments
    probs_segs <- sits_classify(
        data = segments,
        ml_model = rfor_model,
        output_dir = tempdir()
    )
    #
    # Create a classified vector cube
    class_segs <- sits_label_classification(
        cube = probs_segs,
        output_dir = tempdir(),
        multicores = 2,
        memsize = 4
    )
    # plot the segments
    plot(class_segs)
}

sits documentation built on Jan. 14, 2026, 5:06 p.m.