sits (version 1.5.0)

sits_slic: Segment an image using SLIC

Description

Apply a segmentation on a data cube based on the supercells package. This is an adaptation and extension to remote sensing data of the SLIC superpixels algorithm proposed by Achanta et al. (2012). See references for more details.

Usage

sits_slic(
  data = NULL,
  step = 5,
  compactness = 1,
  dist_fun = "euclidean",
  avg_fun = "median",
  iter = 30,
  minarea = 10,
  verbose = FALSE
)

Value

Set of segments for a single tile

Arguments

data

A matrix with time series.

step

Distance (in number of cells) between initial supercells' centers.

compactness

A compactness value. Larger values cause clusters to be more compact/even (square).

dist_fun

Distance function. Currently implemented: euclidean, jsd, dtw, and any distance function from the philentropy package. See philentropy::getDistMethods().

avg_fun

Averaging function to calculate the values of the supercells' centers. Accepts any fitting R function (e.g., base::mean() or stats::median()) or one of internally implemented "mean" and "median". Default: "median"

iter

Number of iterations to create the output.

minarea

Specifies the minimal size of a supercell (in cells).

verbose

Show the progress bar?

Author

Gilberto Camara,

Rolf Simoes,

Felipe Carvalho,

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",
        data_dir = data_dir
    )
    # segment the vector cube
    segments <- sits_segment(
        cube = cube,
        output_dir = tempdir(),
        version = "slic-demo"
    )
    # 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(),
        version = "slic-demo"
    )
    # label the probability segments
    seg_label <- sits_label_classification(
        cube = seg_probs,
        output_dir = tempdir(),
        version = "slic-demo"
    )
}