sits (version 1.5.3)

sits_segment: Segment an image

Description

Apply a spatial-temporal segmentation on a data cube based on a user defined segmentation function. The function applies the segmentation algorithm "seg_fn" to each tile. The output is a vector data cube, which is a data cube with an additional vector file in "geopackage" format.

Usage

sits_segment(
  cube,
  seg_fn = sits_slic(),
  roi = NULL,
  impute_fn = impute_linear(),
  start_date = NULL,
  end_date = NULL,
  memsize = 4L,
  multicores = 2L,
  output_dir,
  version = "v1",
  progress = TRUE
)

Value

A tibble of class 'segs_cube' representing the segmentation.

Arguments

cube

Regular data cube

seg_fn

Function to apply the segmentation

roi

Region of interest (see below)

impute_fn

Imputation function to remove NA values.

start_date

Start date for the segmentation

end_date

End date for the segmentation.

memsize

Memory available for classification (in GB).

multicores

Number of cores to be used for classification.

output_dir

Directory for output file.

version

Version of the output (for multiple segmentations).

progress

Show progress bar?

Author

Gilberto Camara,

Rolf Simoes,

Felipe Carvalho,

Felipe Carlos,

References

Achanta, Radhakrishna, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Süsstrunk. 2012. “SLIC Superpixels Compared to State-of-the-Art Superpixel Methods.” IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (11): 2274–82.

Nowosad, Jakub, and Tomasz F. Stepinski. 2022. “Extended SLIC Superpixels Algorithm for Applications to Non-Imagery Geospatial Rasters.” International Journal of Applied Earth Observation and Geoinformation 112 (August): 102935.

Examples

if (sits_run_examples()) {
    data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
    # create a data cube
    cube <- sits_cube(
        source = "BDC",
        collection = "MOD13Q1-6.1",
        data_dir = data_dir
    )
    # segment the vector cube
    segments <- sits_segment(
        cube = cube,
        seg_fn = sits_slic(
            step = 10,
            compactness = 1,
            dist_fun = "euclidean",
            avg_fun = "median",
            iter = 30,
            minarea = 10
        ),
        output_dir = tempdir()
    )
    # create a classification model
    rfor_model <- sits_train(samples_modis_ndvi, sits_rfor())
    # classify the segments
    seg_probs <- sits_classify(
        data = segments,
        ml_model = rfor_model,
        output_dir = tempdir()
    )
    # label the probability segments
    seg_label <- sits_label_classification(
        cube = seg_probs,
        output_dir = tempdir()
    )
}