plot.class_cube: Plot classified images

In e-sensing/sits: Satellite Image Time Series Analysis for Earth Observation Data Cubes

Plot classified images

Description

plots a classified raster using ggplot.

Usage

## S3 method for class 'class_cube'
plot(
  x,
  y,
  ...,
  tile = x[["tile"]][[1]],
  roi = NULL,
  title = "Classified Image",
  legend = NULL,
  palette = "Spectral",
  scale = 1,
  max_cog_size = 1024,
  legend_position = "inside"
)

Arguments

x	Object of class "class_cube".
y	Ignored.
...	Further specifications for plot.
tile	Tile to be plotted.
roi	Spatial extent to plot in WGS 84 - named vector with either (lon_min, lon_max, lat_min, lat_max) or (xmin, xmax, ymin, ymax)
title	Title of the plot.
legend	Named vector that associates labels to colors.
palette	Alternative RColorBrewer palette
scale	Relative scale (0.4 to 1.0) of plot text
max_cog_size	Maximum size of COG overviews (lines or columns)
legend_position	Where to place the legend (default = "outside")

Value

A color map, where each pixel has the color associated to a label, as defined by the legend parameter.

Note

The following optional parameters are available to allow for detailed control over the plot output:

• graticules_labels_size: size of coordinates labels (default = 0.8)

• legend_title_size: relative size of legend title (default = 1.0)

• legend_text_size: relative size of legend text (default = 1.0)

• legend_bg_color: color of legend background (default = "white")

• legend_bg_alpha: legend opacity (default = 0.5)

Author(s)

Gilberto Camara, [e-mail]

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
    )
    # classify a data cube
    probs_cube <- sits_classify(
        data = cube, ml_model = rfor_model, output_dir = tempdir()
    )
    # label cube with the most likely class
    label_cube <- sits_label_classification(
        probs_cube,
        output_dir = tempdir()
    )
    # plot the resulting classified image
    plot(label_cube)
}

e-sensing/sits documentation built on Feb. 13, 2025, 2:22 a.m.