sits_view: View data cubes and samples in leaflet

Description

Uses leaflet to visualize time series, raster cube and classified images

Usage

sits_view(x, ...)
# S3 method for sits
sits_view(x, ..., legend = NULL, palette = "Harmonic")
# S3 method for data.frame
sits_view(x, ..., legend = NULL, palette = "Harmonic")
# S3 method for som_map
sits_view(x, ..., id_neurons, legend = NULL, palette = "Harmonic")
# S3 method for raster_cube
sits_view(
  x,
  ...,
  band = NULL,
  red = NULL,
  green = NULL,
  blue = NULL,
  tiles = x[["tile"]],
  dates = NULL,
  class_cube = NULL,
  legend = NULL,
  palette = "RdYlGn",
  opacity = 0.7
)
# S3 method for vector_cube
sits_view(
  x,
  ...,
  band = NULL,
  red = NULL,
  green = NULL,
  blue = NULL,
  tiles = x[["tile"]],
  dates = NULL,
  class_cube = NULL,
  legend = NULL,
  palette = "RdYlGn",
  opacity = 0.7,
  seg_color = "black",
  line_width = 1
)
# S3 method for uncertainty_cube
sits_view(
  x,
  ...,
  tiles = x[["tile"]],
  class_cube = NULL,
  legend = NULL,
  palette = "Blues",
  opacity = 0.7
)
# S3 method for class_cube
sits_view(
  x,
  ...,
  tiles = x[["tile"]],
  legend = NULL,
  palette = "Spectral",
  opacity = 0.8
)
# S3 method for probs_cube
sits_view(
  x,
  ...,
  tiles = x[["tile"]],
  class_cube = NULL,
  legend = NULL,
  opacity = 0.7,
  palette = "YlGnBu"
)
# S3 method for default
sits_view(x, ...)

Value

A leaflet object containing either samples or data cubes embedded in a global map that can be visualized directly in an RStudio viewer.

Arguments

x: Object of class "sits", "data.frame", "som_map", "raster_cube" or "classified image".
...: Further specifications for sits_view.
legend: Named vector that associates labels to colors.
palette: Color palette (if colors not in legend nor in sits default colors)
id_neurons: Neurons from the SOM map to be shown.
band: For plotting grey images.
red: Band for red color.
green: Band for green color.
blue: Band for blue color.
tiles: Tiles to be plotted (in case of a multi-tile cube).
dates: Dates to be plotted.
class_cube: Classified cube to be overlayed on top on image.
opacity: Opacity of segment fill or class cube
seg_color: Color for segment boundaries
line_width: Line width for segments (in pixels)

Author

Gilberto Camara, gilberto.camara@inpe.br

Examples

Run this code

if (sits_run_examples()) {
    # view samples
    sits_view(cerrado_2classes)
    # create a local data cube
    data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
    modis_cube <- sits_cube(
        source = "BDC",
        collection = "MOD13Q1-6",
        data_dir = data_dir
    )
    # view the data cube
    sits_view(modis_cube,
        band = "NDVI"
    )
    # train a model
    rf_model <- sits_train(samples_modis_ndvi, sits_rfor())
    # classify the cube
    modis_probs <- sits_classify(
        data = modis_cube,
        ml_model = rf_model,
        output_dir = tempdir()
    )
    # generate a map
    modis_label <- sits_label_classification(
        modis_probs,
        output_dir = tempdir()
    )
    # view the classified map
    sits_view(modis_label)
    # view the classified map with the B/W image
    sits_view(modis_cube,
        band = "NDVI",
        class_cube = modis_label,
        dates = sits_timeline(modis_cube)[[1]]
    )
    # view the classified map with the RGB image
    sits_view(modis_cube,
        red = "NDVI", green = "NDVI", blue = "NDVI",
        class_cube = modis_label,
        dates = sits_timeline(modis_cube)[[1]]
    )
    # create an uncertainty cube
    modis_uncert <- sits_uncertainty(
        cube = modis_probs,
        output_dir = tempdir()
    )
    # view the uncertainty cube
    sits_view(modis_uncert)
}