| plot.class_vector_cube | R Documentation | 
Plot vector classified cube
## S3 method for class 'class_vector_cube'
plot(
  x,
  ...,
  tile = x[["tile"]][[1L]],
  legend = NULL,
  seg_color = "black",
  line_width = 0.5,
  palette = "Spectral",
  scale = 1,
  legend_position = "outside"
)
| x | Object of class "segments". | 
| ... | Further specifications for plot. | 
| tile | Tile to be plotted. | 
| legend | Named vector that associates labels to colors. | 
| seg_color | Segment color. | 
| line_width | Segment line width. | 
| palette | Alternative RColorBrewer palette | 
| scale | Scale to plot map (0.4 to 1.0) | 
| legend_position | Where to place the legend (default = "outside") | 
A plot object with an RGB image or a B/W image on a color scale using the chosen palette
To see which color palettes are supported, please run
Gilberto Camara, gilberto.camara@inpe.br
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)
}
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