Segment an image using SLIC

Apply a segmentation on a data cube using either the supercells or snic packages, depending on the chosen algorithm. As of version 1.5.4, two segmentation methods are supported. The recommended option is SNIC, implemented via the snic package, which applies a non-iterative clustering strategy to generate compact, homogeneous superpixels from uniformly distributed seeds (Achanta and Susstrunk, 2017). The alternative method uses the SLIC algorithm implemented in the supercells package, adapted for remote sensing data following Achanta et al. (2012). This SLIC variant is deprecated and will be removed in a future release. See references for more details.

Usage

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

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?

Value

Set of segments for a single tile

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.

Author

Rolf Simoes, rolfsimoes@gmail.com

Felipe Carvalho, felipe.carvalho@inpe.br

Felipe Carlos, efelipecarlos@gmail.com

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_snic(
            grid_seeding = "rectangular",
            spacing = 10,
            compactness = 0.3,
            padding = 0
        ),
        output_dir = tempdir(),
        version = "snic-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 = "snic-demo"
    )
    # label the probability segments
    seg_label <- sits_label_classification(
        cube = seg_probs,
        output_dir = tempdir(),
        version = "snic-demo"
    )
    plot(seg_label)
}